From 8866557c1f651445f6a51de43687010149c06ac1 Mon Sep 17 00:00:00 2001
From: Ben Clark <ben.clark2@arm.com>
Date: Tue, 15 Oct 2024 16:50:55 +0100
Subject: [PATCH 01/17] temp for moving laptops

---
 .../profiling-ml-on-arm/Streamline.png        | Bin 0 -> 128971 bytes
 .../profiling-ml-on-arm/_index.md             |  39 ++++
 .../profiling-ml-on-arm/_next-steps.md        |  27 +++
 .../profiling-ml-on-arm/_review.md            |  45 ++++
 .../app-profiling-android-studio.md           |  13 ++
 .../app-profiling-streamline.md               | 200 ++++++++++++++++++
 .../profiling-ml-on-arm/example-picture.png   | Bin 0 -> 63167 bytes
 .../nn-profiling-executenetwork.md            |  13 ++
 .../nn-profiling-general.md                   |  13 ++
 .../profiling-ml-on-arm/plan.txt              |  20 ++
 .../profiling-ml-on-arm/why-profile.md        |  23 ++
 11 files changed, 393 insertions(+)
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/Streamline.png
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/plan.txt
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/Streamline.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/Streamline.png
new file mode 100644
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diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
new file mode 100644
index 000000000..2bd02eb5c
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -0,0 +1,39 @@
+---
+title: How to Profile ML on Arm
+
+minutes_to_complete: 30
+
+who_is_this_for: ML App developers who want to see the performance of their models on Arm devices.
+
+learning_objectives: 
+    - Profile timings of ML models on Arm devices.
+    - Profile ML application performance on Arm devices.
+
+prerequisites:
+    - Some application development.
+    - Arm device with Android OS.
+
+author_primary: Ben Clark
+
+### Tags
+skilllevels: Intermediate
+subjects: PLACEHOLDER SUBJECT
+armips:
+    - Cortex-X
+    - Cortex-A
+    - Mali
+    - Immortalis
+tools_software_languages:
+    - Android Studio
+    - tflite
+operatingsystems:
+    - Android
+    - Linux
+
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1                       # _index.md always has weight of 1 to order correctly
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
new file mode 100644
index 000000000..c4ae77cc9
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
@@ -0,0 +1,27 @@
+---
+next_step_guidance: PLACEHOLDER TEXT 1
+
+recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/
+
+further_reading:
+    - resource:
+        title: PLACEHOLDER MANUAL 
+        link: PLACEHOLDER MANUAL LINK
+        type: documentation
+    - resource:
+        title: PLACEHOLDER BLOG 
+        link: PLACEHOLDER BLOG LINK
+        type: blog
+    - resource:
+        title: PLACEHOLDER GENERAL WEBSITE 
+        link: PLACEHOLDER GENERAL WEBSITE LINK
+        type: website
+
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 21                  # set to always be larger than the content in this path, and one more than 'review'
+title: "Next Steps"         # Always the same
+layout: "learningpathall"   # All files under learning paths have this same wrapper
+---
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
new file mode 100644
index 000000000..9b1bfb96f
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
@@ -0,0 +1,45 @@
+---
+review:
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 1?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+        correct_answer: 1                    
+        explanation: >
+            PLACEHOLDER EXPLANATION 1
+
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 2?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+            - PLACEHOLDER ANSWER C
+            - PLACEHOLDER ANSWER D
+        correct_answer: 4                   
+        explanation: >
+            PLACEHOLDER EXPLANATION 2
+               
+    - questions:
+        question: >
+            PLACEHOLDER QUESTION 3?
+        answers:
+            - PLACEHOLDER ANSWER A
+            - PLACEHOLDER ANSWER B
+            - PLACEHOLDER ANSWER C
+            - PLACEHOLDER ANSWER D
+        correct_answer: 2          
+        explanation: >
+            PLACEHOLDER EXPLANATION 3
+
+
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+title: "Review"                 # Always the same title
+weight: 20                      # Set to always be larger than the content in this path
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+---
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
new file mode 100644
index 000000000..1d63e0df0
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
@@ -0,0 +1,13 @@
+---
+title: App Profiling with Android Studio
+weight: 4
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## PLACEHOLDER HEADER OF SECOND STEP
+YOUR CONTENT GOES HERE
+
+IMAGE HERE:
+![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
new file mode 100644
index 000000000..65ccd6eb7
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -0,0 +1,200 @@
+---
+title: App Profiling with Streamline
+weight: 3
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Application Profiling
+Application profiling can be split into 2 main types - *Instrumentation* and *Sampling*. Streamline, for example, is a sampling profiler, that takes regular samples of various counters and registers in the system to provide a detailed view of the system's performance. Sampling will only provide a statistical view, but it is less intrusive and has less processing overhead than instrumentation.
+
+The profiler can look at memory, CPU activity and cycles, cache misses, and may parts of the GPU as well as other performance metrics. It can also provide a timeline view of these counters to show the application's performance over time. This will show bottlenecks, and help you understand where to focus your optimization efforts.
+
+![Streamline image alt-text#center](Streamline.png "Figure 1. Streamline timeline view")
+
+## Streamline
+Streamline (and Performance Studio) will be installed on a host machine, and will connect to your target Arm device to capture the data. In our example, this will be an Android phone. The data is captured over a USB connection, and the data is then analyzed on the host machine.
+
+There are many <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio">tutorials and training videos</a> on Streamline, which you can refer to for more depth. Our example will be Android-based, but you can use <a href="https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application">these instructions for Linux</a> setup and capture.
+
+For now, as per these <a href="https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en">setup instructions</a>, make sure you have `adb` (Android Debug Bridge) installed. We will be looking at Android Studio profiling shortly, and if you have installed Android Studio, it will have installed adb. Otherwise, you can get it as part of the Android SDK platform tools <a href="https://developer.android.com/studio/releases/platform-tools.html">here</a>.
+
+Make sure adb is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the platform-tools directory to your path. The instructions for this will depend on your host machine's operating system.
+
+Next, install <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads">Arm Performance Studio</a>, which includes Streamline. 
+
+Connect your device to your computer through USB. Ensure that your device is set to Developer mode.
+
+On your device, go to `Settings > Developer Options` and enable USB Debugging. If your device asks you to authorize connection to your computer, confirm this. Test the connection by running `adb devices` in a terminal. You should see your device ID listed.
+
+Next, you need a debuggable build of the application you want to profile. 
+- In Android Studio, ensure your *Build Variant* is set to `debug`. You can then build the application and install it on your device. 
+- For a Unity app, select Development Build under File > Build Settings when building your application. 
+- In Unreal Engine, open Project Settings > Project > Packaging > Project, and ensure that the For Distribution checkbox is not set. 
+- In the general case, you can set `android:debuggable=true` in the application manifest file.
+
+You can now run Streamline, and do a capture of your application. This can be used to do a general performance analysis of your application, but we intend to add additional useful information first.
+
+## Custom Annotations
+
+In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. In the picture above there are annotations to show when inference, pre-processing, and post-processing are happening.
+
+To add annotations, we need to add some files into our project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` from <a href="https://github.com/ARM-software/gator/tree/main/annotate">here</a>. Then we will be able to show log strings, markers, counters and Custom Activity Maps.
+
+These files are obviously C code, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is skightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this <a href="https://developer.android.com/studio/projects/add-native-code">here</a>.
+
+For us, we create a file the we will call `annotate_jni_wrapper.c`. This will be a wrapper around the Gator daemon's functions, and will be called from our Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
+
+```c
+#include <jni.h>
+#include "streamline_annotate.h"
+
+JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateSetup(JNIEnv* env, jobject obj) {
+    gator_annotate_setup();
+}
+
+JNIEXPORT jlong JNICALL Java_AnnotateStreamline_GetTime(JNIEnv* env, jobject obj) {
+    return gator_get_time();
+}
+```
+
+Some functions have `unsigned int`, but that needs to be a `jint` in the wrapper, with some casting required in your Kotlin code to enforce type correctness at that end. Some functions have strings as arguments, and you will need to do a small conversion:
+
+```c
+JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateMarkerColorStr(JNIEnv* env, jobject obj, jint color, jstring str) {
+    const char* nativeStr = (*env)->GetStringUTFChars(env, str, 0);
+    gator_annotate_marker_color(color, nativeStr);
+    (*env)->ReleaseStringUTFChars(env, str, nativeStr);
+}
+```
+
+In Android Studio `cmake` is used to create your C library, so we need a CMakelists.txt file in the same directory as the C file. This will look like:
+
+```cmake
+# Sets the minimum CMake version required for this project.
+cmake_minimum_required(VERSION 3.22.1)
+
+# Declare the project name. 
+project("StreamlineAnnotationJNI")
+
+# Create and name the library
+add_library(${CMAKE_PROJECT_NAME} SHARED
+    annotate_jni_wrapper.c
+    streamline_annotate.c)
+
+# Specifies libraries CMake should link to your target library. 
+# Adding in the Android system log library pulls in the NDK path.
+find_library( # Sets the path to the NDK library.
+        log-lib
+        log )
+
+target_link_libraries( # Specifies the target library.
+        ${CMAKE_PROJECT_NAME}
+        ${log-lib} )
+```
+
+If you add the following to your `build.gradle` file, you will be able to call the functions from your Kotlin code:
+
+```gradle
+    externalNativeBuild {
+        cmake {
+            path file('src/main/cpp/CMakeLists.txt')
+            version '3.22.1'
+        }
+    }
+```
+
+This will create a `libStreamlineAnnotationJNI.so` library that you can load in your Kotlin code, and then you can call the functions. Here we create a singleton `AnnotateStreamline.kt` that then enables Kotlin calls from the rest of our code:
+
+```kotlin
+// Kotlin wrapper class for integration into Android project
+class AnnotateStreamline {
+    init {
+    // Load the native library
+        System.loadLibrary("StreamlineAnnotationJNI")
+    }
+
+    companion object {
+    // #defines for colors from the Streamline Annotation c code
+        const val ANNOTATE_RED: UInt = 0x0000ff1bu
+        const val ANNOTATE_BLUE: UInt = 0xff00001bu
+        const val ANNOTATE_GREEN: UInt = 0x00ff001bu
+        const val ANNOTATE_PURPLE: UInt = 0xff00ff1bu
+        const val ANNOTATE_YELLOW: UInt = 0x00ffff1bu
+        // any other constants you want from the included gator files
+
+        // Create an instance of the AnnotateStreamline class
+        private val annotations = AnnotateStreamline()
+
+        // Function to setup the Streamline Annotation - call this first
+        @JvmStatic
+        fun setup() {
+            annotations.AnnotateSetup()
+        }
+
+        // Function to get the current time from gator
+        @JvmStatic
+        fun getTime(): Long {
+            return annotations.GetTime()
+        }
+
+        // more functions that you want, e.g. (note UInt conversion)
+        @JvmStatic
+        fun annotateMarkerColorStr(color: UInt, str: String) {
+            annotations.AnnotateMarkerColorStr(color.toInt(), str)
+        }
+        // ...
+    }
+
+    // externals match the last part of function names in annotate_jni_wrapper.c
+    external fun AnnotateSetup()
+    external fun GetTime(): Long
+    external fun AnnotateMarkerColorStr(color: Int, str: String)
+    // ...
+}
+```
+
+The `AnnotateStreamline` class can now be used in your Kotlin code to add annotations to the Streamline timeline view. Make sure that `AnnotateStreamline.setup()` is called first, and then you can add annotations like this:
+
+```kotlin
+      AnnotateStreamline.annotateMarkerColorStr(AnnotateStreamline.ANNOTATE_BLUE, "Important event")
+```
+
+## Custom Activity Maps (CAMs)
+
+In addition to adding strings to the log and colored markers to the timeline, a particularly useful set of annotations is the Custom Activity Maps. These are the named colored bands you can see at the bottom of the Streamline timeline view shown above. They can be used to show when specific parts of your application are running, such as the pre-processing or inference, and layered for functions within functions etc.
+
+To add these you'll need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you'll need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that in the case of multiple places in your code adding annotations, the ids are unique.
+
+Here is an example setup in a class's companion object.
+
+```kotlin
+  companion object {
+    const val camViewId = 1u
+    const val trackRoot = 1u
+    const val trackChild = 2u
+    baseId = (0u..UInt.MAX_VALUE/2u-5000u).random()
+    currentId = baseId
+
+    init {
+      AnnotateStreamline.camViewName(camViewId, "Inference")
+      AnnotateStreamline.camTrack(camViewId, trackRoot,0xffffffffu, "Root") // root wants -1 for parent id
+      AnnotateStreamline.camTrack(camViewId, trackChild, trackRoot, "Children")
+    }
+```
+
+Then they can be used like this:
+
+```kotlin
+      val preprocess = currentId++
+      AnnotateStreamline.camJobStart(camViewId, preprocess, "Preprocess", trackRoot, AnnotateStreamline.getTime(), AnnotateStreamline.ANNOTATE_YELLOW)
+      val childjob = currentId++
+      AnnotateStreamline.camJobStart(camViewId, childjob, "child job", trackChild, AnnotateStreamline.getTime(), AnnotateStreamline.ANNOTATE_CYAN)
+      //child job code...
+      AnnotateStreamline.camJobEnd(camViewId, childjob, AnnotateStreamline.getTime())
+      //rest of preprocessing code...
+      AnnotateStreamline.camJobEnd(camViewId, preprocess, AnnotateStreamline.getTime())
+```
+
+Now when you build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. This will make it obvious whether the time spent in your application is on the inference, ML pre-processing, ML post-processing, or other parts of your application.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png
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diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
new file mode 100644
index 000000000..551fc2959
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
@@ -0,0 +1,13 @@
+---
+title: ML profiling with ExecuteNetwork
+weight: 6
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## PLACEHOLDER HEADER OF SECOND STEP
+YOUR CONTENT GOES HERE
+
+IMAGE HERE:
+![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
new file mode 100644
index 000000000..ea589f59a
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
@@ -0,0 +1,13 @@
+---
+title: Profiling the Neural Network
+weight: 5
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## PLACEHOLDER HEADER OF SECOND STEP
+YOUR CONTENT GOES HERE
+
+IMAGE HERE:
+![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/plan.txt b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/plan.txt
new file mode 100644
index 000000000..70e766717
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/plan.txt
@@ -0,0 +1,20 @@
+
+want the performance of your ML app
+memory and compute
+
+how can you find that out
+
+different steps:
+- ML network
+- app around the ML network, especially pre and post processing, and the network as a whole
+
+for around the ML network - streamline profiler
+here's how to do that...
+Also Android Profiler, memory example
+
+Ml network, it will depend on the inference engine you are using
+- here's an example for if you are using ArmNN with TFLite
+- if you're not using it, it may still have some useful information, but different operators will be used and their performance will be different
+can see structure with netron or google model explorer to compare operators or different versions of networks
+may need to use a conversion tool to convert to TFLite (or whatever your inference engine wants)
+
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
new file mode 100644
index 000000000..3864750f6
--- /dev/null
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
@@ -0,0 +1,23 @@
+---
+title: What do you need to profile?
+weight: 2
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Performance
+Working out what is taking the time and memory in your application is the first step to getting the performance you want. Profiling can help you identify the bottlenecks in your application and understand how to optimize it.
+
+With Machine Learning (ML) applications, the inference of the Neural Network (NN) itself is often the heaviest part of the application in terms of computation and memory usage. This is not guaranteed however, so it is important to profile the application as a whole to see if pre- or post-processing or other code is an issue.
+
+We will be looking at an Android example using TFLite in this learning path, but most of the tools also work with Linux to cover a wide range of Arm devices. The principles are the same for other inference engines and platforms, but the tools are different.
+
+## Tools
+
+Currently different tools are needed if you want to look inside the NN inference, or at the application's performance as a whole.
+
+For profiling the ML inference we will look at <a href="https://github.com/ARM-software/armnn/releases">ArmNN</a>'s ExecuteNetwork.
+
+For profiling the application as a whole we will look at <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio">Arm Performance Studio</a>'s Streamline, and at the Android Studio Profiler.
+

From a593790d21d0b6c379ba26ddd45671b5a6a4d6ee Mon Sep 17 00:00:00 2001
From: Ben Clark <ben.clark2@arm.com>
Date: Mon, 21 Oct 2024 17:15:15 +0100
Subject: [PATCH 02/17] network profiling and ExecuteNetwork pages

---
 .../profiling-ml-on-arm/_index.md             |   2 +-
 .../app-profiling-android-studio.md           |   6 +-
 .../profiling-ml-on-arm/example-picture.png   | Bin 63167 -> 0 bytes
 .../nn-profiling-executenetwork.md            |  68 ++++++++++++++++--
 .../nn-profiling-general.md                   |  11 +--
 5 files changed, 74 insertions(+), 13 deletions(-)
 delete mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
index 2bd02eb5c..a57070750 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -17,7 +17,7 @@ author_primary: Ben Clark
 
 ### Tags
 skilllevels: Intermediate
-subjects: PLACEHOLDER SUBJECT
+subjects: Machine Learning
 armips:
     - Cortex-X
     - Cortex-A
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
index 1d63e0df0..e2873f0af 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
@@ -6,8 +6,6 @@ weight: 4
 layout: learningpathall
 ---
 
-## PLACEHOLDER HEADER OF SECOND STEP
-YOUR CONTENT GOES HERE
+## Android Memory Profiling
+Memory is often a problem in ML, with ever bigger models and data. For profiling an Android app's memory, Android Studio has a built-in profiler. This can be used to monitor the memory usage of your app, and to find memory leaks.
 
-IMAGE HERE:
-![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/example-picture.png
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diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
index 551fc2959..aeafa6bc1 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
@@ -6,8 +6,68 @@ weight: 6
 layout: learningpathall
 ---
 
-## PLACEHOLDER HEADER OF SECOND STEP
-YOUR CONTENT GOES HERE
+## ArmNN's Network Profiler
+One way of running tflite models is with ArmNN. This is available as a delegate to the standard tflite interpreter. But to profile the model, there is a command-line utility called `ExecuteNetwork`. This program just runs the model without the rest of an app. It is able to output layer timings and other useful information to let you know where there might be bottlenecks within your model.
 
-IMAGE HERE:
-![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
+If you are not using tflite, you'll need to look at other tools from your framework to profile your model. If you are using tflite, but not ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to see if there are any obvious problems.
+
+To get `ExecuteNetwork` you can download it from the [ArmNN GitHub](https://github.com/ARM-software/armnn/releases). Download the version appropriate for the Android phone you wish to test on - the Android version and the architecture of the phone. If you are unsure of the architecture, you can use a lower one, but you may miss out on some optimizations. Inside the tar.gz that you download, `ExecuteNetwork` is included. Note among the other release downloads on the ArmNN Github is the separate file for the `aar` delegate which is the easy way to include the ArmNN delegate into your app.
+
+To run `ExecuteNetwork` you'll need to use `adb` to push the model and the executable to your phone, and then run it from the adb shell. `adb` is included with Android Studio, but you may need to add it to your path. Android Studio normally installs it to a location like \<user>\AppData\Local\Android\Sdk\platform-tools. `adb` can also be downloaded separately from the [Android Developer site](https://developer.android.com/studio/releases/platform-tools).
+
+From a command prompt, you can run the following commands to push the files to your phone:
+
+```bash
+adb push model.tflite /data/local/tmp/
+adb push ExecuteNetwork /data/local/tmp/
+adb push libarm_compute.so /data/local/tmp/
+adb push libarmnn.so /data/local/tmp/
+adb push libarmnn_support_library.so /data/local/tmp/
+```
+Push all the `.so` library files that are in the base folder of the tar.gz you downloaded, alongside `ExecuteNetwork`, and all the `.so` files in the `delegate` sub-folder. If you are using a recent version on of Android Studio this copying can be done much more easily with drag and drop in the *Device Explorer > Files*.
+
+Then you need to set the permissions on the files:
+
+```bash
+adb shell        # to get into the adb shell on the phone
+cd /data/local/tmp
+chmod 777 ExecuteNetwork    # to make the file executable
+chmod 777 *.so	   # to make the library files executable
+```
+
+Then you can run the model with the following command:
+
+```bash
+LD_LIBRARY_PATH=. ./ExecuteNetwork -m model.tflite -c CpuAcc -T delegate --iterations 2 --do-not-print-output --enable-fast-math --fp16-turbo-mode -e --output-network-details > modelout.txt
+```
+
+This will run the model twice, outputting the layer timings to `modelout.txt`. Specifically, the `-e` and `--output-network-details` flags will output a lot of timeline information about the model, including the layer timings. The `--do-not-print-output` flag will stop the output of the model, which can be very large, and without sensible input, meaningless. The `--enable-fast-math` and `--fp16-turbo-mode` flags enable some maths optimizations. The `--iterations 2` flag will run the model twice: the first run includes a lot of startup costs and one-off optimizations, so the second run is more indicative of the real performance.
+
+After running the model, you can pull the output file back to your computer with the following commands:
+
+```bash
+exit         # to leave the adb shell
+adb pull /data/local/tmp/modelout.txt
+```
+Once again, this can be done with drag and drop in Android Studio's *Device Explorer > Files*.
+
+Depending on the size of your model, the output will probably be quite large. You can use a text editor or a tool like `less` to view the file. The output is in JSON format, so you can use a JSON viewer to make it more readable. Usually some scripting can be used to extract the information you need more easily out of the very raw data in the file.
+
+At the top is the summary, with the setup time and inference time of your 2 runs, which will look something like this:
+```text
+Info: ArmNN v33.2.0
+Info: Initialization time: 7.20 ms.
+Info: ArmnnSubgraph creation
+Info: Parse nodes to ArmNN time: 50.99 ms
+Info: Optimize ArmnnSubgraph time: 85.94 ms
+Info: Load ArmnnSubgraph time: 91.11 ms
+Info: Overall ArmnnSubgraph creation time: 228.47 ms
+
+Info: Execution time: 721.91 ms.
+Info: Inference time: 722.02 ms
+
+Info: Execution time: 468.42 ms.
+Info: Inference time: 468.58 ms
+```
+
+After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations & their timings recorded before the network itself, with the layers' "Wall clock time" in microseconds of how long they took to run. These layers and their timings can then be analysed to see which layers, and which operators, took the most time.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
index ea589f59a..da4fe08de 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
@@ -6,8 +6,11 @@ weight: 5
 layout: learningpathall
 ---
 
-## PLACEHOLDER HEADER OF SECOND STEP
-YOUR CONTENT GOES HERE
+## Profiling your model
+App profilers will give you a good overall view of your performance, but often you might want to look inside the model and work out bottlenecks within the network. The network is often the bulk of the time, in which case it will warrant closer analysis. 
 
-IMAGE HERE:
-![example image alt-text#center](example-picture.png "Figure 1. Example image caption")
+General profilers are unable to do this, as there needs to be annotations inside the ML framework code to get the information. It is a large task to write the profiling annotations throughout the framework, so it is easier if we use tools from a framework or inference engine that can already do the job.
+
+Depending on your model the tools available will differ. For example, if you are using TensorFlow Lite, Arm provides the ArmNN delegate that you can run the model with on Linux or Android. ArmNN in turn provides a tool called `ExecuteNetwork` that can run the model and give you layer timings and other useful information.
+
+If you are using PyTorch, you will probably want ExecuTorch on an Android phone, and this also has a profiler available alongside it.

From 3e4ad22a25afae7ad077fb229a0766dddba3debd Mon Sep 17 00:00:00 2001
From: Ben Clark <ben.clark2@arm.com>
Date: Sun, 27 Oct 2024 18:08:00 +0000
Subject: [PATCH 03/17] done, pending final read-through

---
 .../profiling-ml-on-arm/_next-steps.md        |  18 +++------
 .../profiling-ml-on-arm/_review.md            |  38 +++++++++---------
 .../android-profiling-version.png             | Bin 0 -> 18717 bytes
 .../app-profiling-android-studio.md           |  34 ++++++++++++++++
 .../app-profiling-streamline.md               |  20 ++++-----
 .../profiler-jk-allocations-legend.png        | Bin 0 -> 32173 bytes
 .../start-profile-dropdown.png                | Bin 0 -> 6980 bytes
 .../profiling-ml-on-arm/why-profile.md        |   6 +--
 8 files changed, 72 insertions(+), 44 deletions(-)
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/android-profiling-version.png
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/profiler-jk-allocations-legend.png
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/start-profile-dropdown.png

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
index c4ae77cc9..aafea4126 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
@@ -1,21 +1,15 @@
 ---
-next_step_guidance: PLACEHOLDER TEXT 1
+next_step_guidance: Continue Learning
 
-recommended_path: /learning-paths/PLACEHOLDER_CATEGORY/PLACEHOLDER_LEARNING_PATH/
+recommended_path: /learning-paths/smartphones-and-mobile/build-llama3-chat-android-app-using-executorch-and-xnnpack/
+recommended_path: /learning-paths/smartphones-and-mobile/kleidiai-on-android-with-mediapipe-and-xnnpack/
 
 further_reading:
     - resource:
-        title: PLACEHOLDER MANUAL 
-        link: PLACEHOLDER MANUAL LINK
+        title: Arm Streamline User Guide  
+        link: https://developer.arm.com/documentation/101816/latest/
         type: documentation
-    - resource:
-        title: PLACEHOLDER BLOG 
-        link: PLACEHOLDER BLOG LINK
-        type: blog
-    - resource:
-        title: PLACEHOLDER GENERAL WEBSITE 
-        link: PLACEHOLDER GENERAL WEBSITE LINK
-        type: website
+
 
 
 # ================================================================================
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
index 9b1bfb96f..814fe6c32 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
@@ -2,37 +2,37 @@
 review:
     - questions:
         question: >
-            PLACEHOLDER QUESTION 1?
+            Streamline Profiling lets you profile:
         answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
-        correct_answer: 1                    
+            - Arm CPU activity
+            - Arm GPU activity
+            - when your Neural Network is running
+            - All of the above
+        correct_answer: 4                    
         explanation: >
-            PLACEHOLDER EXPLANATION 1
+            Streamline will show you CPU and GPU activity (and a lot more counters!), and if Custom Activity Maps are used, you can see when your Neural Network and other parts of your application are running.
 
     - questions:
         question: >
-            PLACEHOLDER QUESTION 2?
+            Does Android Studio have a profiler?
         answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
-            - PLACEHOLDER ANSWER C
-            - PLACEHOLDER ANSWER D
-        correct_answer: 4                   
+            - Yes
+            - No
+        correct_answer: 1                   
         explanation: >
-            PLACEHOLDER EXPLANATION 2
+            Yes, Android Studio has a built-in profiler that can be used to monitor the memory usage of your app among other things
                
     - questions:
         question: >
-            PLACEHOLDER QUESTION 3?
+            Is there a way to profile what is happening inside your Neural Network?
         answers:
-            - PLACEHOLDER ANSWER A
-            - PLACEHOLDER ANSWER B
-            - PLACEHOLDER ANSWER C
-            - PLACEHOLDER ANSWER D
-        correct_answer: 2          
+            - Yes, Streamline just shows you out of the box
+            - No
+            - Yes, ArmNN's ExecuteNetwork can do this
+            - Yes, Android Studio Profiler can do this
+        correct_answer: 3          
         explanation: >
-            PLACEHOLDER EXPLANATION 3
+            Standard profilers don't have an easy way to see what is happening inside an ML framework to see a model running inside it. ArmNN's ExecuteNetwork can do this for TensorFlow Lite models, and ExecuTorch has tools that can do this for PyTorch models.
 
 
 
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/android-profiling-version.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/android-profiling-version.png
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diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
index e2873f0af..03ba77f99 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
@@ -9,3 +9,37 @@ layout: learningpathall
 ## Android Memory Profiling
 Memory is often a problem in ML, with ever bigger models and data. For profiling an Android app's memory, Android Studio has a built-in profiler. This can be used to monitor the memory usage of your app, and to find memory leaks.
 
+To find the Profiler, open your project in Android Studio and click on the *View* menu, then *Tool Windows*, and then *Profiler*. This opens the Profiler window. Attach your device, and then you should be able to select your app's process. Here there are a number of different profiling tasks available.
+
+Most likely with an Android ML app you'll need to look at memory both from the Java/Kotlin side and the native side. The Java/Kotlin side is where the app runs, and may be where buffers are allocated for input and output if you're using LiteRT (formerly TensorFlow Lite). The native side is where the ML framework will run. Looking at the memory consumption for Java/Kotlin and native is 2 separate tasks in the Profiler: *Track Memory Consumption (Java/Kotlin Allocations)* and *Track Memory Consumption (Native Allocations)*.
+
+Before we start either task we want to build our profileable app. The instructions for this and for general profiling setup can be found [here](https://developer.android.com/studio/profile). We will want to start the correct profiling version of the app depending on the task.
+
+![Android Studio profiling run types alt-text#center](android-profiling-version.png "Figure 1. Profiling run versions")
+
+For the Java/Kotlin side, you want the **debuggable** "Profile 'app' with complete data", which is based off the debug variant. For the native side, you want the **profileable** "Profile 'app' with low overhead", which is based off the release variant.
+
+### Java/Kotlin
+
+If we start looking at the [Java/Kotlin side](https://developer.android.com/studio/profile/record-java-kotlin-allocations), choose *Profiler: Run 'app' as debuggable*, and then select the *Track Memory Consumption (Java/Kotlin Allocations)* task. Navigate to the part of the app you wish to profile and then we can start profiling. At the bottom of the Profiling window it should look like Figure 2 below. Click *Start Profiler Task*.
+
+![Android Studio Start Profile alt-text#center](start-profile-dropdown.png "Figure 2. Start Profile")
+
+When you're ready, *Stop* the profiling again. Now there'll be a nice timeline graph of memory usage. Unfortunately, while Android Studio has a nicer interface for the Java/Kotlin side than the native side, the key to the timeline graph may be missing. This key is shown in below in Figure 3.
+![Android Studio memory key alt-text#center](profiler-jk-allocations-legend.png "Figure 3. Memory key for the Java/Kotlin Memory Timeline")
+
+The default height of the Profiling view is usually too small, so that will need adjusting to get a sensible graph. You can click at different points of the graph to see the memory allocations at that time. If you look according to the key you can see how much memory is allocated by Java, Native, Graphics, Code etc.
+
+Looking further down you can see the Table of Java/Kotlin allocations for your selected time on the timeline. With ML a lot of your allocations are likely to be byte[] for byte buffers, or possibly int[] for image data, etc. Clicking on the data type will open up the particular allocations, showing their size and when they were allocated. This will help to quickly narrow down their use, and whether they are all needed etc.
+
+### Native
+
+For the [native side](https://developer.android.com/studio/profile/record-native-allocations), the process is similar but with different options. Choose *Profiler: Run 'app' as profileable*, and then select the *Track Memory Consumption (Native Allocations)* task. Here you need to *Start profiler task from: Process Start*. Choose *Stop* once you've captured enough data.
+
+The Native view doesn't have the same nice timeline graph as the Java/Kotlin side, but it does have the Table and Visualization tabs. The Table tab no longer has a list of allocations, but options to *Arrange by allocation method* or *callstack*. Choose callstack and then you can trace down which functions were allocating signifcant memory. Potentially more useful, you can also see Remaining Size. 
+
+In the Visualization tab you can see the callstack as a graph, and once again you can look at total Allocations Size or Remaining Size. If you look at Remaining Size, you can see what is still allocated at the end of the profiling, and by looking a few steps up the stack, probably see which allocations are related to the ML model, by seeing functions that relate to the framework you are using. A lot of the memory may be allocated by that framework rather than in your code, and you may not have much control over it, but it is useful to know where the memory is going.
+
+## Other platforms
+
+On other platforms, you will need a different memory profiler. The objective of working out where the memory is being used is the same, and whether there are issues with leaks or just too much memory being used. There are often trade-offs between memory and speed, and they can be considered more sensibly if the numbers involved are known.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index 65ccd6eb7..af5a13a97 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -9,22 +9,22 @@ layout: learningpathall
 ## Application Profiling
 Application profiling can be split into 2 main types - *Instrumentation* and *Sampling*. Streamline, for example, is a sampling profiler, that takes regular samples of various counters and registers in the system to provide a detailed view of the system's performance. Sampling will only provide a statistical view, but it is less intrusive and has less processing overhead than instrumentation.
 
-The profiler can look at memory, CPU activity and cycles, cache misses, and may parts of the GPU as well as other performance metrics. It can also provide a timeline view of these counters to show the application's performance over time. This will show bottlenecks, and help you understand where to focus your optimization efforts.
+The profiler can look at memory, CPU activity and cycles, cache misses, and many parts of the GPU as well as other performance metrics. It can also provide a timeline view of these counters to show the application's performance over time. This will show bottlenecks, and help you understand where to focus your optimization efforts.
 
 ![Streamline image alt-text#center](Streamline.png "Figure 1. Streamline timeline view")
 
 ## Streamline
-Streamline (and Performance Studio) will be installed on a host machine, and will connect to your target Arm device to capture the data. In our example, this will be an Android phone. The data is captured over a USB connection, and the data is then analyzed on the host machine.
+[Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads) (and Performance Studio) will be installed on a host machine, and will connect to your target Arm device to capture the data. In our example, this will be an Android phone. The data is captured over a USB connection, and then analyzed on the host machine.
 
-There are many <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio">tutorials and training videos</a> on Streamline, which you can refer to for more depth. Our example will be Android-based, but you can use <a href="https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application">these instructions for Linux</a> setup and capture.
+There are many [tutorials and training videos](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) on Streamline, which you can refer to for more depth. Our example will be Android-based, but you can use [these instructions for Linux](https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application) setup and capture.
 
-For now, as per these <a href="https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en">setup instructions</a>, make sure you have `adb` (Android Debug Bridge) installed. We will be looking at Android Studio profiling shortly, and if you have installed Android Studio, it will have installed adb. Otherwise, you can get it as part of the Android SDK platform tools <a href="https://developer.android.com/studio/releases/platform-tools.html">here</a>.
+For now, as per these [setup instructions](https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en), make sure you have `adb` (Android Debug Bridge) installed. We will be looking at Android Studio profiling shortly, and if you have installed Android Studio, it will have installed adb. Otherwise, you can get it as part of the Android SDK platform tools [here](https://developer.android.com/studio/releases/platform-tools.html).
 
-Make sure adb is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the platform-tools directory to your path. The instructions for this will depend on your host machine's operating system.
+Make sure adb is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the `platform-tools` directory to your path. The instructions for this (and location of the directory) will depend on your host machine's operating system.
 
-Next, install <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads">Arm Performance Studio</a>, which includes Streamline. 
+Next, install [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads), which includes Streamline. 
 
-Connect your device to your computer through USB. Ensure that your device is set to Developer mode.
+Connect your Android phone to your computer through USB. Ensure that your device is set to [Developer mode](https://developer.android.com/studio/debug/dev-options).
 
 On your device, go to `Settings > Developer Options` and enable USB Debugging. If your device asks you to authorize connection to your computer, confirm this. Test the connection by running `adb devices` in a terminal. You should see your device ID listed.
 
@@ -38,11 +38,11 @@ You can now run Streamline, and do a capture of your application. This can be us
 
 ## Custom Annotations
 
-In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. In the picture above there are annotations to show when inference, pre-processing, and post-processing are happening.
+In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of the picture above there are annotations to show when inference, pre-processing, and post-processing are happening.
 
-To add annotations, we need to add some files into our project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` from <a href="https://github.com/ARM-software/gator/tree/main/annotate">here</a>. Then we will be able to show log strings, markers, counters and Custom Activity Maps.
+To add annotations, we need to add some files into our project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` from [here](https://github.com/ARM-software/gator/tree/main/annotate). Then we will be able to show log strings, markers, counters and Custom Activity Maps.
 
-These files are obviously C code, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is skightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this <a href="https://developer.android.com/studio/projects/add-native-code">here</a>.
+These files are obviously C code, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
 
 For us, we create a file the we will call `annotate_jni_wrapper.c`. This will be a wrapper around the Gator daemon's functions, and will be called from our Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
 
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/profiler-jk-allocations-legend.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/profiler-jk-allocations-legend.png
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diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
index 3864750f6..8a0d20d1a 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
@@ -15,9 +15,9 @@ We will be looking at an Android example using TFLite in this learning path, but
 
 ## Tools
 
-Currently different tools are needed if you want to look inside the NN inference, or at the application's performance as a whole.
+Currently, you need to use different tools to profile the ML inference or the application's performance as a whole.
 
-For profiling the ML inference we will look at <a href="https://github.com/ARM-software/armnn/releases">ArmNN</a>'s ExecuteNetwork.
+For profiling the ML inference we will look at [ArmNN](https://github.com/ARM-software/armnn/releases)'s ExecuteNetwork.
 
-For profiling the application as a whole we will look at <a href="https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio">Arm Performance Studio</a>'s Streamline, and at the Android Studio Profiler.
+For profiling the application as a whole we will look at [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio)'s Streamline, and at the Android Studio Profiler.
 

From faac583d06636047bcab940010d635951dc95dc8 Mon Sep 17 00:00:00 2001
From: Ben Clark <ben.clark2@arm.com>
Date: Mon, 28 Oct 2024 13:47:35 +0000
Subject: [PATCH 04/17] editing on self-review

---
 .../profiling-ml-on-arm/_index.md               |   2 +-
 .../profiling-ml-on-arm/_review.md              |   2 +-
 .../app-profiling-android-studio.md             |  16 ++++++++--------
 .../app-profiling-streamline.md                 |  16 ++++++++++------
 .../nn-profiling-executenetwork.md              |  15 ++++++++-------
 .../profiling-ml-on-arm/nn-profiling-general.md |   2 +-
 .../profiling-ml-on-arm/streamline_marker.png   | Bin 0 -> 52790 bytes
 7 files changed, 29 insertions(+), 24 deletions(-)
 create mode 100644 content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/streamline_marker.png

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
index a57070750..593c7e111 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -3,7 +3,7 @@ title: How to Profile ML on Arm
 
 minutes_to_complete: 30
 
-who_is_this_for: ML App developers who want to see the performance of their models on Arm devices.
+who_is_this_for: Machine Learning App developers who want to see the performance of their models on Arm devices.
 
 learning_objectives: 
     - Profile timings of ML models on Arm devices.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
index 814fe6c32..a1f46d1ed 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
@@ -27,7 +27,7 @@ review:
             Is there a way to profile what is happening inside your Neural Network?
         answers:
             - Yes, Streamline just shows you out of the box
-            - No
+            - No.
             - Yes, ArmNN's ExecuteNetwork can do this
             - Yes, Android Studio Profiler can do this
         correct_answer: 3          
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
index 03ba77f99..7d49a7303 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
@@ -1,5 +1,5 @@
 ---
-title: App Profiling with Android Studio
+title: Memory Profiling with Android Studio
 weight: 4
 
 ### FIXED, DO NOT MODIFY
@@ -9,9 +9,9 @@ layout: learningpathall
 ## Android Memory Profiling
 Memory is often a problem in ML, with ever bigger models and data. For profiling an Android app's memory, Android Studio has a built-in profiler. This can be used to monitor the memory usage of your app, and to find memory leaks.
 
-To find the Profiler, open your project in Android Studio and click on the *View* menu, then *Tool Windows*, and then *Profiler*. This opens the Profiler window. Attach your device, and then you should be able to select your app's process. Here there are a number of different profiling tasks available.
+To find the Profiler, open your project in Android Studio and click on the *View* menu, then *Tool Windows*, and then *Profiler*. This opens the Profiler window. Attach your device in Developer Mode with a USB cable, and then you should be able to select your app's process. Here there are a number of different profiling tasks available.
 
-Most likely with an Android ML app you'll need to look at memory both from the Java/Kotlin side and the native side. The Java/Kotlin side is where the app runs, and may be where buffers are allocated for input and output if you're using LiteRT (formerly TensorFlow Lite). The native side is where the ML framework will run. Looking at the memory consumption for Java/Kotlin and native is 2 separate tasks in the Profiler: *Track Memory Consumption (Java/Kotlin Allocations)* and *Track Memory Consumption (Native Allocations)*.
+Most likely with an Android ML app you'll need to look at memory both from the Java/Kotlin side and the native side. The Java/Kotlin side is where the app runs, and may be where buffers are allocated for input and output if, for example, you're using LiteRT (formerly known as TensorFlow Lite). The native side is where the ML framework will run. Looking at the memory consumption for Java/Kotlin and native is 2 separate tasks in the Profiler: *Track Memory Consumption (Java/Kotlin Allocations)* and *Track Memory Consumption (Native Allocations)*.
 
 Before we start either task we want to build our profileable app. The instructions for this and for general profiling setup can be found [here](https://developer.android.com/studio/profile). We will want to start the correct profiling version of the app depending on the task.
 
@@ -25,18 +25,18 @@ If we start looking at the [Java/Kotlin side](https://developer.android.com/stud
 
 ![Android Studio Start Profile alt-text#center](start-profile-dropdown.png "Figure 2. Start Profile")
 
-When you're ready, *Stop* the profiling again. Now there'll be a nice timeline graph of memory usage. Unfortunately, while Android Studio has a nicer interface for the Java/Kotlin side than the native side, the key to the timeline graph may be missing. This key is shown in below in Figure 3.
+When you're ready, *Stop* the profiling again. Now there'll be a nice timeline graph of memory usage. Unfortunately, while Android Studio has a nicer interface for the Java/Kotlin side than the native side, the key to the timeline graph may be missing. This key is shown below in Figure 3, so you can refer to the colors from this.
 ![Android Studio memory key alt-text#center](profiler-jk-allocations-legend.png "Figure 3. Memory key for the Java/Kotlin Memory Timeline")
 
-The default height of the Profiling view is usually too small, so that will need adjusting to get a sensible graph. You can click at different points of the graph to see the memory allocations at that time. If you look according to the key you can see how much memory is allocated by Java, Native, Graphics, Code etc.
+The default height of the Profiling view - as well as the timeline graph within it - is usually too small, so adjust these heights to get a sensible graph. You can click at different points of the graph to see the memory allocations at that time. If you look according to the key you can see how much memory is allocated by Java, Native, Graphics, Code etc.
 
-Looking further down you can see the Table of Java/Kotlin allocations for your selected time on the timeline. With ML a lot of your allocations are likely to be byte[] for byte buffers, or possibly int[] for image data, etc. Clicking on the data type will open up the particular allocations, showing their size and when they were allocated. This will help to quickly narrow down their use, and whether they are all needed etc.
+Looking further down you can see the *Table* of Java/Kotlin allocations for your selected time on the timeline. With ML a lot of your allocations are likely to be byte[] for byte buffers, or possibly int[] for image data, etc. Clicking on the data type will open up the particular allocations, showing their size and when they were allocated. This will help to quickly narrow down their use, and whether they are all needed etc.
 
 ### Native
 
-For the [native side](https://developer.android.com/studio/profile/record-native-allocations), the process is similar but with different options. Choose *Profiler: Run 'app' as profileable*, and then select the *Track Memory Consumption (Native Allocations)* task. Here you need to *Start profiler task from: Process Start*. Choose *Stop* once you've captured enough data.
+For the [native side](https://developer.android.com/studio/profile/record-native-allocations), the process is similar but with different options. Choose *Profiler: Run 'app' as profileable*, and then select the *Track Memory Consumption (Native Allocations)* task. Here you have to *Start profiler task from: Process Start*. Choose *Stop* once you've captured enough data.
 
-The Native view doesn't have the same nice timeline graph as the Java/Kotlin side, but it does have the Table and Visualization tabs. The Table tab no longer has a list of allocations, but options to *Arrange by allocation method* or *callstack*. Choose callstack and then you can trace down which functions were allocating signifcant memory. Potentially more useful, you can also see Remaining Size. 
+The Native view doesn't have the same nice timeline graph as the Java/Kotlin side, but it does have the *Table* and *Visualization* tabs. The *Table* tab no longer has a list of allocations, but options to *Arrange by allocation method* or *callstack*. Choose *Arrange by callstack* and then you can trace down which functions were allocating signifcant memory. Potentially more useful, you can also see Remaining Size. 
 
 In the Visualization tab you can see the callstack as a graph, and once again you can look at total Allocations Size or Remaining Size. If you look at Remaining Size, you can see what is still allocated at the end of the profiling, and by looking a few steps up the stack, probably see which allocations are related to the ML model, by seeing functions that relate to the framework you are using. A lot of the memory may be allocated by that framework rather than in your code, and you may not have much control over it, but it is useful to know where the memory is going.
 
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index af5a13a97..976a3d0b7 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -38,13 +38,13 @@ You can now run Streamline, and do a capture of your application. This can be us
 
 ## Custom Annotations
 
-In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of the picture above there are annotations to show when inference, pre-processing, and post-processing are happening.
+In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of *Figure 1* above there are custom annotations to show when inference, pre-processing, and post-processing are happening.
 
 To add annotations, we need to add some files into our project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` from [here](https://github.com/ARM-software/gator/tree/main/annotate). Then we will be able to show log strings, markers, counters and Custom Activity Maps.
 
 These files are obviously C code, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
 
-For us, we create a file the we will call `annotate_jni_wrapper.c`. This will be a wrapper around the Gator daemon's functions, and will be called from our Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
+For us, we create a file that we will call `annotate_jni_wrapper.c` in the `src/main/cpp/` folder under our project. This will be a wrapper around the Gator daemon's functions, and will be called from our Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
 
 ```c
 #include <jni.h>
@@ -94,7 +94,7 @@ target_link_libraries( # Specifies the target library.
         ${log-lib} )
 ```
 
-If you add the following to your `build.gradle` file, you will be able to call the functions from your Kotlin code:
+If you add the following to the `build.gradle` file of the Module you wish to profile, you will be able to call the functions from your Kotlin code:
 
 ```gradle
     externalNativeBuild {
@@ -158,12 +158,16 @@ class AnnotateStreamline {
 The `AnnotateStreamline` class can now be used in your Kotlin code to add annotations to the Streamline timeline view. Make sure that `AnnotateStreamline.setup()` is called first, and then you can add annotations like this:
 
 ```kotlin
-      AnnotateStreamline.annotateMarkerColorStr(AnnotateStreamline.ANNOTATE_BLUE, "Important event")
+      AnnotateStreamline.annotateMarkerColorStr(AnnotateStreamline.ANNOTATE_BLUE, "Model Load")
 ```
 
+This colored marker with a string will add the string and time to Streamline's log view, and look like the below image in Streamline's timeline:
+
+![Streamline image alt-text#center](streamline_marker.png "Figure 2. Streamline timeline markers")
+
 ## Custom Activity Maps (CAMs)
 
-In addition to adding strings to the log and colored markers to the timeline, a particularly useful set of annotations is the Custom Activity Maps. These are the named colored bands you can see at the bottom of the Streamline timeline view shown above. They can be used to show when specific parts of your application are running, such as the pre-processing or inference, and layered for functions within functions etc.
+In addition to adding strings to the log and colored markers to the timeline, a particularly useful set of annotations is the Custom Activity Maps. These are the named colored bands you can see at the bottom of the Streamline timeline view shown in *Figure 1*. They can be used to show when specific parts of your application are running, such as the pre-processing or inference, and layered for functions within functions etc.
 
 To add these you'll need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you'll need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that in the case of multiple places in your code adding annotations, the ids are unique.
 
@@ -197,4 +201,4 @@ Then they can be used like this:
       AnnotateStreamline.camJobEnd(camViewId, preprocess, AnnotateStreamline.getTime())
 ```
 
-Now when you build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. This will make it obvious whether the time spent in your application is on the inference, ML pre-processing, ML post-processing, or other parts of your application.
+Now when you build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. Once the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
index aeafa6bc1..125474190 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
@@ -7,24 +7,25 @@ layout: learningpathall
 ---
 
 ## ArmNN's Network Profiler
-One way of running tflite models is with ArmNN. This is available as a delegate to the standard tflite interpreter. But to profile the model, there is a command-line utility called `ExecuteNetwork`. This program just runs the model without the rest of an app. It is able to output layer timings and other useful information to let you know where there might be bottlenecks within your model.
+One way of running tflite models is with ArmNN. This is available as a delegate to the standard tflite interpreter. But to profile the model, ArmNN comes with a command-line utility called `ExecuteNetwork`. This program just runs the model without the rest of an app. It is able to output layer timings and other useful information to let you know where there might be bottlenecks within your model.
 
-If you are not using tflite, you'll need to look at other tools from your framework to profile your model. If you are using tflite, but not ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to see if there are any obvious problems.
+If you are not using tflite, you'll need to look at other tools from your framework to profile your model. If you are using tflite without ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to see if there are any obvious problems.
 
 To get `ExecuteNetwork` you can download it from the [ArmNN GitHub](https://github.com/ARM-software/armnn/releases). Download the version appropriate for the Android phone you wish to test on - the Android version and the architecture of the phone. If you are unsure of the architecture, you can use a lower one, but you may miss out on some optimizations. Inside the tar.gz that you download, `ExecuteNetwork` is included. Note among the other release downloads on the ArmNN Github is the separate file for the `aar` delegate which is the easy way to include the ArmNN delegate into your app.
 
 To run `ExecuteNetwork` you'll need to use `adb` to push the model and the executable to your phone, and then run it from the adb shell. `adb` is included with Android Studio, but you may need to add it to your path. Android Studio normally installs it to a location like \<user>\AppData\Local\Android\Sdk\platform-tools. `adb` can also be downloaded separately from the [Android Developer site](https://developer.android.com/studio/releases/platform-tools).
 
-From a command prompt, you can run the following commands to push the files to your phone:
+Unzip the tar.gz folder you downloaded to somewhere convenient. From a command prompt, you can then adapt and run the following commands to push the files to your phone. The `/data/local/tmp` folder of your Android device is a place with relaxed permissions that you can use to run this profiling.
 
 ```bash
-adb push model.tflite /data/local/tmp/
+adb push model.tflite /data/local/tmp/   # the tflite NN model file that you wish to profile
 adb push ExecuteNetwork /data/local/tmp/
 adb push libarm_compute.so /data/local/tmp/
 adb push libarmnn.so /data/local/tmp/
 adb push libarmnn_support_library.so /data/local/tmp/
+# more ArmNN .so library files
 ```
-Push all the `.so` library files that are in the base folder of the tar.gz you downloaded, alongside `ExecuteNetwork`, and all the `.so` files in the `delegate` sub-folder. If you are using a recent version on of Android Studio this copying can be done much more easily with drag and drop in the *Device Explorer > Files*.
+Push all the `.so` library files that are in the base folder of the tar.gz you downloaded, alongside `ExecuteNetwork`, and all the `.so` files in the `delegate` sub-folder. If you are using a recent version of Android Studio this copying can be done much more easily with drag and drop in the *Device Explorer > Files*.
 
 Then you need to set the permissions on the files:
 
@@ -51,7 +52,7 @@ adb pull /data/local/tmp/modelout.txt
 ```
 Once again, this can be done with drag and drop in Android Studio's *Device Explorer > Files*.
 
-Depending on the size of your model, the output will probably be quite large. You can use a text editor or a tool like `less` to view the file. The output is in JSON format, so you can use a JSON viewer to make it more readable. Usually some scripting can be used to extract the information you need more easily out of the very raw data in the file.
+Depending on the size of your model, the output will probably be quite large. You can use a text editor to view the file. The output is in JSON format, so you can use a JSON viewer to make it more readable. Usually some scripting can be used to extract the information you need more easily out of the very raw data in the file.
 
 At the top is the summary, with the setup time and inference time of your 2 runs, which will look something like this:
 ```text
@@ -70,4 +71,4 @@ Info: Execution time: 468.42 ms.
 Info: Inference time: 468.58 ms
 ```
 
-After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations & their timings recorded before the network itself, with the layers' "Wall clock time" in microseconds of how long they took to run. These layers and their timings can then be analysed to see which layers, and which operators, took the most time.
+After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations and their timings recorded before the network itself, so you can skip past the graph and the optimization timings to get to the part that wants analyzing.  The layers' "Wall clock time" in microseconds shows how long they took to run. These layers and their timings can then be analyzed to see which layers, and which operators, took the most time.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
index da4fe08de..7ca228759 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
@@ -11,6 +11,6 @@ App profilers will give you a good overall view of your performance, but often y
 
 General profilers are unable to do this, as there needs to be annotations inside the ML framework code to get the information. It is a large task to write the profiling annotations throughout the framework, so it is easier if we use tools from a framework or inference engine that can already do the job.
 
-Depending on your model the tools available will differ. For example, if you are using TensorFlow Lite, Arm provides the ArmNN delegate that you can run the model with on Linux or Android. ArmNN in turn provides a tool called `ExecuteNetwork` that can run the model and give you layer timings and other useful information.
+Depending on your model the tools available will differ. For example, if you are using LiteRT (formerly TensorFlow Lite), Arm provides the ArmNN delegate that you can run the model with on Linux or Android, CPU or GPU. ArmNN in turn provides a tool called `ExecuteNetwork` that can run the model and give you layer timings and other useful information.
 
 If you are using PyTorch, you will probably want ExecuTorch on an Android phone, and this also has a profiler available alongside it.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/streamline_marker.png b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/streamline_marker.png
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From 6f58ebe28f7d114143f893d66713bad1e5dab121 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Mon, 4 Nov 2024 09:11:34 -0500
Subject: [PATCH 05/17] Update _index.md

---
 .../profiling-ml-on-arm/_index.md                     | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
index 593c7e111..1c0e05223 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -1,22 +1,21 @@
 ---
-title: How to Profile ML on Arm
+title: Profile the performance of ML models on Arm
 
 minutes_to_complete: 30
 
-who_is_this_for: Machine Learning App developers who want to see the performance of their models on Arm devices.
+who_is_this_for: This is an introductory topic for software developers who want to learn how to profile the performance of their ML models running on Arm devices.
 
 learning_objectives: 
-    - Profile timings of ML models on Arm devices.
+    - Profile the execution times of ML models on Arm devices.
     - Profile ML application performance on Arm devices.
 
 prerequisites:
-    - Some application development.
-    - Arm device with Android OS.
+    - An Arm-powered Android smartphone.
 
 author_primary: Ben Clark
 
 ### Tags
-skilllevels: Intermediate
+skilllevels: Introductory
 subjects: Machine Learning
 armips:
     - Cortex-X

From 1c9ff903299f163763370724055f588be7f3461a Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Mon, 4 Nov 2024 10:11:23 -0500
Subject: [PATCH 06/17] Update _index.md

---
 .../smartphones-and-mobile/profiling-ml-on-arm/_index.md        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
index 1c0e05223..3bd9669e9 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -16,7 +16,7 @@ author_primary: Ben Clark
 
 ### Tags
 skilllevels: Introductory
-subjects: Machine Learning
+subjects: ML
 armips:
     - Cortex-X
     - Cortex-A

From f87f6dd5ef55dce5feaefe96fed3a6bdd2d6dcfb Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Mon, 4 Nov 2024 10:17:52 -0500
Subject: [PATCH 07/17] Update why-profile.md

---
 .../profiling-ml-on-arm/why-profile.md                 | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
index 8a0d20d1a..75ac1724f 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
@@ -1,5 +1,5 @@
 ---
-title: What do you need to profile?
+title: Why do you need to profile your ML application?
 weight: 2
 
 ### FIXED, DO NOT MODIFY
@@ -11,13 +11,13 @@ Working out what is taking the time and memory in your application is the first
 
 With Machine Learning (ML) applications, the inference of the Neural Network (NN) itself is often the heaviest part of the application in terms of computation and memory usage. This is not guaranteed however, so it is important to profile the application as a whole to see if pre- or post-processing or other code is an issue.
 
-We will be looking at an Android example using TFLite in this learning path, but most of the tools also work with Linux to cover a wide range of Arm devices. The principles are the same for other inference engines and platforms, but the tools are different.
+In this Learning Path, you will be profiling at an Android example using TFLite, but most of the tools also work with Linux and cover a wide range of Arm devices. The principles for profiling your application are the same for use with other inference engines and platforms, but the tools are different.
 
 ## Tools
 
-Currently, you need to use different tools to profile the ML inference or the application's performance as a whole.
+You will need to use different tools to profile the ML inference or the application's performance running on your Arm device.
 
-For profiling the ML inference we will look at [ArmNN](https://github.com/ARM-software/armnn/releases)'s ExecuteNetwork.
+For profiling the ML inference, you will use [ArmNN](https://github.com/ARM-software/armnn/releases)'s ExecuteNetwork.
 
-For profiling the application as a whole we will look at [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio)'s Streamline, and at the Android Studio Profiler.
+For profiling the application as a whole, you will use [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio)'s Streamline, and at the Android Studio Profiler.
 

From 1c8d85fb52f9cbfb7b16f3b1d83ac907052c99de Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Mon, 4 Nov 2024 10:37:16 -0500
Subject: [PATCH 08/17] Update app-profiling-streamline.md

---
 .../app-profiling-streamline.md               | 32 +++++++++----------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index 976a3d0b7..86fd7702f 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -1,5 +1,5 @@
 ---
-title: App Profiling with Streamline
+title: Profile your application with Streamline
 weight: 3
 
 ### FIXED, DO NOT MODIFY
@@ -7,26 +7,26 @@ layout: learningpathall
 ---
 
 ## Application Profiling
-Application profiling can be split into 2 main types - *Instrumentation* and *Sampling*. Streamline, for example, is a sampling profiler, that takes regular samples of various counters and registers in the system to provide a detailed view of the system's performance. Sampling will only provide a statistical view, but it is less intrusive and has less processing overhead than instrumentation.
+Application profiling can be split into 2 main types - *Instrumentation* and *Sampling*. [Streamline](https://developer.arm.com/Tools%20and%20Software/Streamline%20Performance%20Analyzer), for example, is a sampling profiler, that takes regular samples of various counters and registers in the system to provide a detailed view of the system's performance. Sampling will only provide a statistical view, but it is less intrusive and has less processing overhead than instrumentation.
 
 The profiler can look at memory, CPU activity and cycles, cache misses, and many parts of the GPU as well as other performance metrics. It can also provide a timeline view of these counters to show the application's performance over time. This will show bottlenecks, and help you understand where to focus your optimization efforts.
 
 ![Streamline image alt-text#center](Streamline.png "Figure 1. Streamline timeline view")
 
 ## Streamline
-[Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads) (and Performance Studio) will be installed on a host machine, and will connect to your target Arm device to capture the data. In our example, this will be an Android phone. The data is captured over a USB connection, and then analyzed on the host machine.
+You will install [Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads) (and Performance Studio) on your host machine, and will connect to your target Arm device to capture the data. In this example, the target device is an Arm-powered Android phone. The data is captured over a USB connection, and then analyzed on your host machine.
 
-There are many [tutorials and training videos](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) on Streamline, which you can refer to for more depth. Our example will be Android-based, but you can use [these instructions for Linux](https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application) setup and capture.
+There are many [tutorials and training videos](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) on Streamline, which you can refer to for more depth. This example will be Android based, but you can use [these instructions for Linux](https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application) setup and capture.
 
-For now, as per these [setup instructions](https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en), make sure you have `adb` (Android Debug Bridge) installed. We will be looking at Android Studio profiling shortly, and if you have installed Android Studio, it will have installed adb. Otherwise, you can get it as part of the Android SDK platform tools [here](https://developer.android.com/studio/releases/platform-tools.html).
+First, follow these [setup instructions](https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en), to make sure you have `adb` (Android Debug Bridge) installed. If you have installed [Android Studio](https://developer.android.com/studio), you will have installed adb already. Otherwise, you can get it as part of the Android SDK platform tools [here](https://developer.android.com/studio/releases/platform-tools.html).
 
-Make sure adb is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the `platform-tools` directory to your path. The instructions for this (and location of the directory) will depend on your host machine's operating system.
+Make sure `adb` is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the Android SDK `platform-tools` directory to your path. The instructions for this (and location of the directory) will depend on your host machine's operating system.
 
 Next, install [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads), which includes Streamline. 
 
-Connect your Android phone to your computer through USB. Ensure that your device is set to [Developer mode](https://developer.android.com/studio/debug/dev-options).
+Connect your Android phone to your host machine through USB. Ensure that your Android phone is set to [Developer mode](https://developer.android.com/studio/debug/dev-options).
 
-On your device, go to `Settings > Developer Options` and enable USB Debugging. If your device asks you to authorize connection to your computer, confirm this. Test the connection by running `adb devices` in a terminal. You should see your device ID listed.
+On your phone, go to `Settings > Developer Options` and enable USB Debugging. If your phone asks you to authorize connection to your host machine, confirm this. Test the connection by running `adb devices` in a terminal. You should see your device ID listed.
 
 Next, you need a debuggable build of the application you want to profile. 
 - In Android Studio, ensure your *Build Variant* is set to `debug`. You can then build the application and install it on your device. 
@@ -34,17 +34,17 @@ Next, you need a debuggable build of the application you want to profile.
 - In Unreal Engine, open Project Settings > Project > Packaging > Project, and ensure that the For Distribution checkbox is not set. 
 - In the general case, you can set `android:debuggable=true` in the application manifest file.
 
-You can now run Streamline, and do a capture of your application. This can be used to do a general performance analysis of your application, but we intend to add additional useful information first.
+You can now run Streamline, and do a capture of your application. This can be used to do a general performance analysis of your application, but you will add some useful information first.
 
 ## Custom Annotations
 
 In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of *Figure 1* above there are custom annotations to show when inference, pre-processing, and post-processing are happening.
 
-To add annotations, we need to add some files into our project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` from [here](https://github.com/ARM-software/gator/tree/main/annotate). Then we will be able to show log strings, markers, counters and Custom Activity Maps.
+To add annotations, you will need to add some files into your project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` available [here](https://github.com/ARM-software/gator/tree/main/annotate). Then you will be able to show log strings, markers, counters and Custom Activity Maps.
 
-These files are obviously C code, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
+These files are written in C, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
 
-For us, we create a file that we will call `annotate_jni_wrapper.c` in the `src/main/cpp/` folder under our project. This will be a wrapper around the Gator daemon's functions, and will be called from our Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
+Create a file that you will call `annotate_jni_wrapper.c` in the `src/main/cpp/` folder under your project. This will be a wrapper around the Gator daemon's functions, and will be called from your Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
 
 ```c
 #include <jni.h>
@@ -69,7 +69,7 @@ JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateMarkerColorStr(JNIEnv* en
 }
 ```
 
-In Android Studio `cmake` is used to create your C library, so we need a CMakelists.txt file in the same directory as the C file. This will look like:
+In Android Studio `cmake` is used to create your C library, so you will need a `CMakelists.txt` file in the same directory as the C file. This will look like:
 
 ```cmake
 # Sets the minimum CMake version required for this project.
@@ -105,7 +105,7 @@ If you add the following to the `build.gradle` file of the Module you wish to pr
     }
 ```
 
-This will create a `libStreamlineAnnotationJNI.so` library that you can load in your Kotlin code, and then you can call the functions. Here we create a singleton `AnnotateStreamline.kt` that then enables Kotlin calls from the rest of our code:
+This will create a `libStreamlineAnnotationJNI.so` library that you can load in your Kotlin code, and then you can call the functions. Here you will create a singleton `AnnotateStreamline.kt` that then enables Kotlin calls from the rest of your code:
 
 ```kotlin
 // Kotlin wrapper class for integration into Android project
@@ -169,9 +169,9 @@ This colored marker with a string will add the string and time to Streamline's l
 
 In addition to adding strings to the log and colored markers to the timeline, a particularly useful set of annotations is the Custom Activity Maps. These are the named colored bands you can see at the bottom of the Streamline timeline view shown in *Figure 1*. They can be used to show when specific parts of your application are running, such as the pre-processing or inference, and layered for functions within functions etc.
 
-To add these you'll need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you'll need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that in the case of multiple places in your code adding annotations, the ids are unique.
+To add these you will need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you will need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that in the case of multiple places in your code adding annotations, the ids are unique.
 
-Here is an example setup in a class's companion object.
+Here is an example setup in a class's companion object:
 
 ```kotlin
   companion object {

From e8c1c1bea1ef25dbcbc00dbdfb87e00c8ac6b389 Mon Sep 17 00:00:00 2001
From: Ben Clark <ben.clark2@arm.com>
Date: Thu, 7 Nov 2024 13:19:58 +0000
Subject: [PATCH 09/17] added example (app/network) information

---
 .../profiling-ml-on-arm/_index.md             |  4 +-
 .../app-profiling-streamline.md               | 60 ++++++++++++++++---
 .../nn-profiling-executenetwork.md            | 21 +++++--
 3 files changed, 69 insertions(+), 16 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
index 3bd9669e9..1271520b0 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_index.md
@@ -1,7 +1,7 @@
 ---
 title: Profile the performance of ML models on Arm
 
-minutes_to_complete: 30
+minutes_to_complete: 60
 
 who_is_this_for: This is an introductory topic for software developers who want to learn how to profile the performance of their ML models running on Arm devices.
 
@@ -10,7 +10,7 @@ learning_objectives:
     - Profile ML application performance on Arm devices.
 
 prerequisites:
-    - An Arm-powered Android smartphone.
+    - An Arm-powered Android smartphone, and USB cable to connect with it.
 
 author_primary: Ben Clark
 
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index 86fd7702f..294365aac 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -13,6 +13,8 @@ The profiler can look at memory, CPU activity and cycles, cache misses, and many
 
 ![Streamline image alt-text#center](Streamline.png "Figure 1. Streamline timeline view")
 
+If you want a ready-made application to profile for this section you can use [this example](https://github.com/dawidborycki/Arm.PyTorch.MNIST.Inference). Clone it to your computer and open it in a recent Android Studio. It is generally safest to not update the Gradle version when asked, to ensure nothing else needs updating. 
+
 ## Streamline
 You will install [Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads) (and Performance Studio) on your host machine, and will connect to your target Arm device to capture the data. In this example, the target device is an Arm-powered Android phone. The data is captured over a USB connection, and then analyzed on your host machine.
 
@@ -34,17 +36,19 @@ Next, you need a debuggable build of the application you want to profile.
 - In Unreal Engine, open Project Settings > Project > Packaging > Project, and ensure that the For Distribution checkbox is not set. 
 - In the general case, you can set `android:debuggable=true` in the application manifest file.
 
-You can now run Streamline, and do a capture of your application. This can be used to do a general performance analysis of your application, but you will add some useful information first.
+If you're using the example application, the Build Variant is `debug` by default, but you can verify this by going to `Build > Select Build Variant` in Android Studio. Build and install the application on your device.
+
+You can now run Streamline, and do a [capture](https://developer.arm.com/documentation/102477/0900/Capture-a-profile?lang=en) of your application. This can be used to do a general performance analysis of your application, but you will add some useful information first.
 
 ## Custom Annotations
 
 In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of *Figure 1* above there are custom annotations to show when inference, pre-processing, and post-processing are happening.
 
-To add annotations, you will need to add some files into your project from the Gator daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` available [here](https://github.com/ARM-software/gator/tree/main/annotate). Then you will be able to show log strings, markers, counters and Custom Activity Maps.
+To add annotations, you will need to add some files into your project from the **gator** daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` available [here](https://github.com/ARM-software/gator/tree/main/annotate). Then you will be able to show log strings, markers, counters and Custom Activity Maps. If you are using the example project, create a `cpp` folder under the `app/src/main` folder, and add these files there.
 
 These files are written in C, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
 
-Create a file that you will call `annotate_jni_wrapper.c` in the `src/main/cpp/` folder under your project. This will be a wrapper around the Gator daemon's functions, and will be called from your Kotlin code. It starts as below and continues with very similar wrapper functions for the other Gator daemon functions you want.
+Create a file in the `app/src/main/cpp/` folder under your project and name it `annotate_jni_wrapper.c`. This will be a wrapper around the gator daemon's functions, and will be called from your Kotlin code. It starts as below and continues with very similar wrapper functions for the other gator daemon functions you want.
 
 ```c
 #include <jni.h>
@@ -69,7 +73,7 @@ JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateMarkerColorStr(JNIEnv* en
 }
 ```
 
-In Android Studio `cmake` is used to create your C library, so you will need a `CMakelists.txt` file in the same directory as the C file. This will look like:
+In Android Studio `cmake` is used to create your C library, so you will need a `CMakelists.txt` file in the same directory as the C files (`app/src/main/cpp/` in the example). This will look like:
 
 ```cmake
 # Sets the minimum CMake version required for this project.
@@ -94,7 +98,7 @@ target_link_libraries( # Specifies the target library.
         ${log-lib} )
 ```
 
-If you add the following to the `build.gradle` file of the Module you wish to profile, you will be able to call the functions from your Kotlin code:
+If you add the following to the `build.gradle` file of the Module you wish to profile (`:app` in the example), you will be able to call the functions from your Kotlin code:
 
 ```gradle
     externalNativeBuild {
@@ -105,7 +109,7 @@ If you add the following to the `build.gradle` file of the Module you wish to pr
     }
 ```
 
-This will create a `libStreamlineAnnotationJNI.so` library that you can load in your Kotlin code, and then you can call the functions. Here you will create a singleton `AnnotateStreamline.kt` that then enables Kotlin calls from the rest of your code:
+This will create a `libStreamlineAnnotationJNI.so` library that you can load in your Kotlin code, and then you can call the functions. Here you will create a singleton `AnnotateStreamline.kt`. Place the file alongside `MainActivity.kt` in `app\src\main\java\com\arm\armpytorchmnistinference` for the example. Add the following code to `AnnotateStreamline.kt` to enable Kotlin calls to the gator daemon from the rest of your code:
 
 ```kotlin
 // Kotlin wrapper class for integration into Android project
@@ -155,13 +159,17 @@ class AnnotateStreamline {
 }
 ```
 
-The `AnnotateStreamline` class can now be used in your Kotlin code to add annotations to the Streamline timeline view. Make sure that `AnnotateStreamline.setup()` is called first, and then you can add annotations like this:
+Fill in all the function calls to match the functions you added into `annotate_jni_wrapper.c`.
+
+The `AnnotateStreamline` class can now be used in your Kotlin code to add annotations to the Streamline timeline view. The first thing is to make sure `AnnotateStreamline.setup()` is called before any other gator functions. For the example project, add it into the `onCreate()` function of `MainActivity.kt`. Then you can add annotations like this:
 
 ```kotlin
       AnnotateStreamline.annotateMarkerColorStr(AnnotateStreamline.ANNOTATE_BLUE, "Model Load")
 ```
 
-This colored marker with a string will add the string and time to Streamline's log view, and look like the below image in Streamline's timeline:
+In the example app you could add this in the `onCreate()` function of `MainActivity.kt` after the `Module.load()` call to load the `model.pth`.
+
+This 'colored marker with a string' annotation will add the string and time to Streamline's log view, and look like the below image in Streamline's timeline (in the example app ArmNN isn't used, so there are no white ArmNN markers):
 
 ![Streamline image alt-text#center](streamline_marker.png "Figure 2. Streamline timeline markers")
 
@@ -188,6 +196,8 @@ Here is an example setup in a class's companion object:
     }
 ```
 
+If you're using the example app, add this to the `MainActivity` class.
+
 Then they can be used like this:
 
 ```kotlin
@@ -201,4 +211,36 @@ Then they can be used like this:
       AnnotateStreamline.camJobEnd(camViewId, preprocess, AnnotateStreamline.getTime())
 ```
 
-Now when you build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. Once the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.
+In the example app the CAM annotations would be added to the `runInference()` function, which would end up looking like this:
+
+```kotlin
+    private fun runInference(bitmap: Bitmap) {
+        val preprocess = currentId++
+        AnnotateStreamline.camJobStart(camViewId, preprocess, "Preprocess", trackRoot, AnnotateStreamline.getTime(), AnnotateStreamline.ANNOTATE_YELLOW)
+        // Convert bitmap to a float array and create a tensor with shape [1, 1, 28, 28]
+        val inputTensor = createTensorFromBitmap(bitmap)   // could add a child CAM job inside function call, but probably too simple
+        AnnotateStreamline.camJobEnd(camViewId, preprocess, AnnotateStreamline.getTime())
+
+        // Run inference and measure time
+        val inferenceTimeMicros = measureTimeMicros {
+            // Forward pass through the model
+            val inference = currentId++
+            AnnotateStreamline.camJobStart(camViewId, inference, "Inference", trackRoot, AnnotateStreamline.getTime(), AnnotateStreamline.ANNOTATE_RED)
+            val outputTensor = model.forward(IValue.from(inputTensor)).toTensor()
+            AnnotateStreamline.camJobEnd(camViewId, inference, AnnotateStreamline.getTime())
+            // and then post-processing is simplistic in this case, so not worth a CAM job
+            val scores = outputTensor.dataAsFloatArray
+
+            // Get the index of the class with the highest score
+            val maxIndex = scores.indices.maxByOrNull { scores[it] } ?: -1
+            predictedLabel.text = "Predicted Label: $maxIndex"
+        }
+
+        // Update inference time TextView in microseconds
+        inferenceTime.text = "Inference Time: $inferenceTimeMicros µs"
+    }
+```
+
+The example app is very fast and simple, so the CAMs will not show much, but in a more complex application you could add more CAMs, including child-level ones, to give more detailed annotations to show where time is spent in your application. Indeed for the example app with its very fast inference, you will be best in Streamline to put the timeline view on the 10µs scale to see the CAM annotations better.
+
+Once you've added in useful CAM annotations, you can build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. Once the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.
diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
index 125474190..b046364a8 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
@@ -11,6 +11,8 @@ One way of running tflite models is with ArmNN. This is available as a delegate
 
 If you are not using tflite, you'll need to look at other tools from your framework to profile your model. If you are using tflite without ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to see if there are any obvious problems.
 
+If you want to have a tflite to practice this Learning Path with, you can download one from the [Arm Model Zoo](https://github.com/ARM-software/ML-zoo). As an example we'll use a [mobilenet tflite](https://github.com/ARM-software/ML-zoo/blob/master/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/mobilenet_v2_1.0_224_INT8.tflite).
+
 To get `ExecuteNetwork` you can download it from the [ArmNN GitHub](https://github.com/ARM-software/armnn/releases). Download the version appropriate for the Android phone you wish to test on - the Android version and the architecture of the phone. If you are unsure of the architecture, you can use a lower one, but you may miss out on some optimizations. Inside the tar.gz that you download, `ExecuteNetwork` is included. Note among the other release downloads on the ArmNN Github is the separate file for the `aar` delegate which is the easy way to include the ArmNN delegate into your app.
 
 To run `ExecuteNetwork` you'll need to use `adb` to push the model and the executable to your phone, and then run it from the adb shell. `adb` is included with Android Studio, but you may need to add it to your path. Android Studio normally installs it to a location like \<user>\AppData\Local\Android\Sdk\platform-tools. `adb` can also be downloaded separately from the [Android Developer site](https://developer.android.com/studio/releases/platform-tools).
@@ -18,7 +20,8 @@ To run `ExecuteNetwork` you'll need to use `adb` to push the model and the execu
 Unzip the tar.gz folder you downloaded to somewhere convenient. From a command prompt, you can then adapt and run the following commands to push the files to your phone. The `/data/local/tmp` folder of your Android device is a place with relaxed permissions that you can use to run this profiling.
 
 ```bash
-adb push model.tflite /data/local/tmp/   # the tflite NN model file that you wish to profile
+adb push mobilenet_v2_1.0_224_INT8.tflite /data/local/tmp/ # if you are using the example tflite model
+# adb push yourmodel.tflite /data/local/tmp/ # where yourmodel is the tflite NN model file that you wish to profile
 adb push ExecuteNetwork /data/local/tmp/
 adb push libarm_compute.so /data/local/tmp/
 adb push libarmnn.so /data/local/tmp/
@@ -36,13 +39,17 @@ chmod 777 ExecuteNetwork    # to make the file executable
 chmod 777 *.so	   # to make the library files executable
 ```
 
-Then you can run the model with the following command:
+Then you can run ExecuteNetwork to profile the model. If you are using the example tflite, you can use the following command:
 
 ```bash
-LD_LIBRARY_PATH=. ./ExecuteNetwork -m model.tflite -c CpuAcc -T delegate --iterations 2 --do-not-print-output --enable-fast-math --fp16-turbo-mode -e --output-network-details > modelout.txt
+LD_LIBRARY_PATH=. ./ExecuteNetwork -m mobilenet_v2_1.0_224_INT8.tflite -c CpuAcc -T delegate --iterations 2 --do-not-print-output --enable-fast-math --fp16-turbo-mode -e --output-network-details > modelout.txt
 ```
 
-This will run the model twice, outputting the layer timings to `modelout.txt`. Specifically, the `-e` and `--output-network-details` flags will output a lot of timeline information about the model, including the layer timings. The `--do-not-print-output` flag will stop the output of the model, which can be very large, and without sensible input, meaningless. The `--enable-fast-math` and `--fp16-turbo-mode` flags enable some maths optimizations. The `--iterations 2` flag will run the model twice: the first run includes a lot of startup costs and one-off optimizations, so the second run is more indicative of the real performance.
+If you are using your own tflite, replace `mobilenet_v2_1.0_224_INT8.tflite` with the name of your tflite file.
+
+This will run the model twice, outputting the layer timings to `modelout.txt`. The `--iterations 2` flag is the command that means it runs twice: the first run includes a lot of startup costs and one-off optimizations, so the second run is more indicative of the real performance.
+
+For the other flags, of note are the `-e` and `--output-network-details` flags which will output a lot of timeline information about the model, including the layer timings. The `--do-not-print-output` flag will stop the output of the model, which can be very large, and without sensible input, meaningless. The `--enable-fast-math` and `--fp16-turbo-mode` flags enable some maths optimizations. `CpuAcc` is the acclerated CPU backend, it can be replaced with `GpuAcc` for the accelerated GPU backend. 
 
 After running the model, you can pull the output file back to your computer with the following commands:
 
@@ -71,4 +78,8 @@ Info: Execution time: 468.42 ms.
 Info: Inference time: 468.58 ms
 ```
 
-After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations and their timings recorded before the network itself, so you can skip past the graph and the optimization timings to get to the part that wants analyzing.  The layers' "Wall clock time" in microseconds shows how long they took to run. These layers and their timings can then be analyzed to see which layers, and which operators, took the most time.
+After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations and their timings recorded before the network itself, so you can skip past the graph and the optimization timings to get to the part that wants analyzing.  
+
+In the mobilenet example output, the graph is from lines 18 to 1629. After this come the optimization timings, which are part of the runtime, but not the network - these go until line 1989. Next there are a few wall clock recordings for the loading of the network, before the first layer "Convolution2dLayer_CreateWorkload_#18" at line 2036. Here is where the layer info that wants analyzing starts.
+
+The layers' "Wall clock time" in microseconds shows how long they took to run. These layers and their timings can then be analyzed to see which layers, and which operators, took the most time.
\ No newline at end of file

From c57c3d0f0b390bc857ab3157ee86232600b524e9 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 11:14:56 -0500
Subject: [PATCH 10/17] Update why-profile.md

---
 .../smartphones-and-mobile/profiling-ml-on-arm/why-profile.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
index 75ac1724f..7d688a4ad 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/why-profile.md
@@ -11,7 +11,7 @@ Working out what is taking the time and memory in your application is the first
 
 With Machine Learning (ML) applications, the inference of the Neural Network (NN) itself is often the heaviest part of the application in terms of computation and memory usage. This is not guaranteed however, so it is important to profile the application as a whole to see if pre- or post-processing or other code is an issue.
 
-In this Learning Path, you will be profiling at an Android example using TFLite, but most of the tools also work with Linux and cover a wide range of Arm devices. The principles for profiling your application are the same for use with other inference engines and platforms, but the tools are different.
+In this Learning Path, you will profile an Android example using TFLite, but most of the steps shown will also work with Linux and cover a wide range of Arm devices. The principles for profiling your application are the same for use with other inference engines and platforms, but the tools are different.
 
 ## Tools
 
@@ -19,5 +19,5 @@ You will need to use different tools to profile the ML inference or the applicat
 
 For profiling the ML inference, you will use [ArmNN](https://github.com/ARM-software/armnn/releases)'s ExecuteNetwork.
 
-For profiling the application as a whole, you will use [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio)'s Streamline, and at the Android Studio Profiler.
+For profiling the application as a whole, you will use [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio)'s Streamline, and the Android Studio Profiler.
 

From a03f8357d8fd1d833beb79dfb8dccc3174972d8a Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 12:13:30 -0500
Subject: [PATCH 11/17] Update app-profiling-streamline.md

---
 .../app-profiling-streamline.md                 | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index 294365aac..a6ebb950b 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -13,16 +13,19 @@ The profiler can look at memory, CPU activity and cycles, cache misses, and many
 
 ![Streamline image alt-text#center](Streamline.png "Figure 1. Streamline timeline view")
 
-If you want a ready-made application to profile for this section you can use [this example](https://github.com/dawidborycki/Arm.PyTorch.MNIST.Inference). Clone it to your computer and open it in a recent Android Studio. It is generally safest to not update the Gradle version when asked, to ensure nothing else needs updating. 
+## Example Android Application 
+
+In this Learning Path, you will use profile [an example Android application](https://github.com/dawidborycki/Arm.PyTorch.MNIST.Inference) using Streamline. 
+Start by cloning the repository containing this example on your machine and open it in a recent Android Studio. It is generally safest to not update the Gradle version when prompted. 
 
 ## Streamline
-You will install [Streamline](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads) (and Performance Studio) on your host machine, and will connect to your target Arm device to capture the data. In this example, the target device is an Arm-powered Android phone. The data is captured over a USB connection, and then analyzed on your host machine.
+You will install Streamline and Performance Studio on your host machine and connect to your target Arm device to capture the data. In this example, the target device is an Arm-powered Android phone. The data is captured over a USB connection, and then analyzed on your host machine.
 
-There are many [tutorials and training videos](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) on Streamline, which you can refer to for more depth. This example will be Android based, but you can use [these instructions for Linux](https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application) setup and capture.
+For more details on Streamline usage you can refer to these [tutorials and training videos](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio). While the example you are running is based on Android, you can use [the setup and capture instructions for Linux](https://developer.arm.com/documentation/101816/0903/Getting-started-with-Streamline/Profile-your-Linux-application).
 
 First, follow these [setup instructions](https://developer.arm.com/documentation/102477/0900/Setup-tasks?lang=en), to make sure you have `adb` (Android Debug Bridge) installed. If you have installed [Android Studio](https://developer.android.com/studio), you will have installed adb already. Otherwise, you can get it as part of the Android SDK platform tools [here](https://developer.android.com/studio/releases/platform-tools.html).
 
-Make sure `adb` is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by adding the Android SDK `platform-tools` directory to your path. The instructions for this (and location of the directory) will depend on your host machine's operating system.
+Make sure `adb` is in your path. You can check this by running `adb` in a terminal. If it is not in your path, you can add it by installing the [Android SDK `platform-tools`](https://developer.android.com/tools/releases/platform-tools#downloads) directory to your path. 
 
 Next, install [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio#Downloads), which includes Streamline. 
 
@@ -36,7 +39,7 @@ Next, you need a debuggable build of the application you want to profile.
 - In Unreal Engine, open Project Settings > Project > Packaging > Project, and ensure that the For Distribution checkbox is not set. 
 - In the general case, you can set `android:debuggable=true` in the application manifest file.
 
-If you're using the example application, the Build Variant is `debug` by default, but you can verify this by going to `Build > Select Build Variant` in Android Studio. Build and install the application on your device.
+For the example application that you cloned earlier, the Build Variant is `debug` by default, but you can verify this by going to `Build > Select Build Variant` in Android Studio. Build and install the application on your device.
 
 You can now run Streamline, and do a [capture](https://developer.arm.com/documentation/102477/0900/Capture-a-profile?lang=en) of your application. This can be used to do a general performance analysis of your application, but you will add some useful information first.
 
@@ -237,10 +240,10 @@ In the example app the CAM annotations would be added to the `runInference()` fu
         }
 
         // Update inference time TextView in microseconds
-        inferenceTime.text = "Inference Time: $inferenceTimeMicros µs"
+        inferenceTime.text = "Inference Time: $inferenceTimeMicros µs"
     }
 ```
 
-The example app is very fast and simple, so the CAMs will not show much, but in a more complex application you could add more CAMs, including child-level ones, to give more detailed annotations to show where time is spent in your application. Indeed for the example app with its very fast inference, you will be best in Streamline to put the timeline view on the 10µs scale to see the CAM annotations better.
+The example app is very fast and simple, so the CAMs will not show much, but in a more complex application you could add more CAMs, including child-level ones, to give more detailed annotations to show where time is spent in your application. Indeed for the example app with its very fast inference, you will be best in Streamline to put the timeline view on the 10µs scale to see the CAM annotations better.
 
 Once you've added in useful CAM annotations, you can build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. Once the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.

From 4289d2e6f895f0aa1a3e839b75281ab0d59058c5 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 15:12:50 -0500
Subject: [PATCH 12/17] Update app-profiling-streamline.md

---
 .../app-profiling-streamline.md               | 30 +++++++++----------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
index a6ebb950b..e55e4e172 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-streamline.md
@@ -39,19 +39,19 @@ Next, you need a debuggable build of the application you want to profile.
 - In Unreal Engine, open Project Settings > Project > Packaging > Project, and ensure that the For Distribution checkbox is not set. 
 - In the general case, you can set `android:debuggable=true` in the application manifest file.
 
-For the example application that you cloned earlier, the Build Variant is `debug` by default, but you can verify this by going to `Build > Select Build Variant` in Android Studio. Build and install the application on your device.
+For the example application that you cloned earlier, the Build Variant is `debug` by default, but you can verify this by going to `Build > Select Build Variant` in Android Studio. Build and install this application on your device.
 
-You can now run Streamline, and do a [capture](https://developer.arm.com/documentation/102477/0900/Capture-a-profile?lang=en) of your application. This can be used to do a general performance analysis of your application, but you will add some useful information first.
+You can now run Streamline and [capture a profile](https://developer.arm.com/documentation/102477/0900/Capture-a-profile?lang=en) of your application. But before you do, lets add some useful annotations to your code that can help with more specific performance analysis of your application.
 
 ## Custom Annotations
 
-In Streamline it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of *Figure 1* above there are custom annotations to show when inference, pre-processing, and post-processing are happening.
+In Streamline, it is possible to add custom annotations to the timeline view. This can be useful to mark the start and end of specific parts of your application, or to mark when a specific event occurs. This can help you understand the performance of your application in relation to these events. At the bottom of *Figure 1* above there are custom annotations to show when inference, pre-processing, and post-processing are happening.
 
-To add annotations, you will need to add some files into your project from the **gator** daemon that Streamline uses. These files are `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` available [here](https://github.com/ARM-software/gator/tree/main/annotate). Then you will be able to show log strings, markers, counters and Custom Activity Maps. If you are using the example project, create a `cpp` folder under the `app/src/main` folder, and add these files there.
+To add annotations, you will need to add some files into your project from the **gator** daemon that Streamline uses. These files are named `streamline_annotate.c`, `streamline_annotate.h` and `streamline_annotate_logging.h` and made available [here](https://github.com/ARM-software/gator/tree/main/annotate). Using these annotations, you will be able to show log strings, markers, counters and Custom Activity Maps. WIthin your example project, create a `cpp` folder under the `app/src/main` folder, and add these three files there.
 
 These files are written in C, so if your Android Studio project is in Java or Kotlin, you will need to add a C library to your project. This is slightly trickier than just adding a Java or Kotlin file, but it is not difficult. You can find instructions on how to do this [here](https://developer.android.com/studio/projects/add-native-code).
 
-Create a file in the `app/src/main/cpp/` folder under your project and name it `annotate_jni_wrapper.c`. This will be a wrapper around the gator daemon's functions, and will be called from your Kotlin code. It starts as below and continues with very similar wrapper functions for the other gator daemon functions you want.
+Create a file in the `app/src/main/cpp/` folder under your project and name it `annotate_jni_wrapper.c`. This will be a wrapper around the gator daemon's functions, and will be called from your Kotlin code. Copy the code below into this file. You can also create very similar wrapper functions for other gator daemon functions.
 
 ```c
 #include <jni.h>
@@ -66,7 +66,7 @@ JNIEXPORT jlong JNICALL Java_AnnotateStreamline_GetTime(JNIEnv* env, jobject obj
 }
 ```
 
-Some functions have `unsigned int`, but that needs to be a `jint` in the wrapper, with some casting required in your Kotlin code to enforce type correctness at that end. Some functions have strings as arguments, and you will need to do a small conversion:
+Some functions have `unsigned int`, but that needs to be a `jint` in the wrapper, with some casting required in your Kotlin code to enforce type correctness at that end. Some functions have strings as arguments, and you will need to do a small conversion as shown below:
 
 ```c
 JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateMarkerColorStr(JNIEnv* env, jobject obj, jint color, jstring str) {
@@ -76,7 +76,7 @@ JNIEXPORT void JNICALL Java_AnnotateStreamline_AnnotateMarkerColorStr(JNIEnv* en
 }
 ```
 
-In Android Studio `cmake` is used to create your C library, so you will need a `CMakelists.txt` file in the same directory as the C files (`app/src/main/cpp/` in the example). This will look like:
+In Android Studio `cmake` is used to create your C library, so you will need a `CMakelists.txt` file in the same directory as the C files (`app/src/main/cpp/` in the example). Copy the contents shown below into `CMakelists.txt`:
 
 ```cmake
 # Sets the minimum CMake version required for this project.
@@ -101,7 +101,7 @@ target_link_libraries( # Specifies the target library.
         ${log-lib} )
 ```
 
-If you add the following to the `build.gradle` file of the Module you wish to profile (`:app` in the example), you will be able to call the functions from your Kotlin code:
+Now add the code below to the `build.gradle` file of the Module you wish to profile (`:app` in the example). You will be able to call the functions from your Kotlin code:
 
 ```gradle
     externalNativeBuild {
@@ -172,7 +172,7 @@ The `AnnotateStreamline` class can now be used in your Kotlin code to add annota
 
 In the example app you could add this in the `onCreate()` function of `MainActivity.kt` after the `Module.load()` call to load the `model.pth`.
 
-This 'colored marker with a string' annotation will add the string and time to Streamline's log view, and look like the below image in Streamline's timeline (in the example app ArmNN isn't used, so there are no white ArmNN markers):
+This 'colored marker with a string' annotation will add the string and time to Streamline's log view, and look like the image shown below in Streamline's timeline (in the example app ArmNN isn't used, so there are no white ArmNN markers):
 
 ![Streamline image alt-text#center](streamline_marker.png "Figure 2. Streamline timeline markers")
 
@@ -180,7 +180,7 @@ This 'colored marker with a string' annotation will add the string and time to S
 
 In addition to adding strings to the log and colored markers to the timeline, a particularly useful set of annotations is the Custom Activity Maps. These are the named colored bands you can see at the bottom of the Streamline timeline view shown in *Figure 1*. They can be used to show when specific parts of your application are running, such as the pre-processing or inference, and layered for functions within functions etc.
 
-To add these you will need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you will need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that in the case of multiple places in your code adding annotations, the ids are unique.
+To add these you will need to import the functions that start `gator_cam_` from `streamline_annotate.h` through your wrapper files in the same way as the functions above. Then you can use CAMs, but first you will need to set up the tracks the annotations will appear on and an id system for each annotation. The `baseId` code below is to ensure that if you add annotations in multiple places in your code, the ids are unique.
 
 Here is an example setup in a class's companion object:
 
@@ -199,9 +199,9 @@ Here is an example setup in a class's companion object:
     }
 ```
 
-If you're using the example app, add this to the `MainActivity` class.
+For the example app, add this to the `MainActivity` class.
 
-Then they can be used like this:
+Then it can be used like this:
 
 ```kotlin
       val preprocess = currentId++
@@ -214,7 +214,7 @@ Then they can be used like this:
       AnnotateStreamline.camJobEnd(camViewId, preprocess, AnnotateStreamline.getTime())
 ```
 
-In the example app the CAM annotations would be added to the `runInference()` function, which would end up looking like this:
+In the example app, the CAM annotations are added to the `runInference()` function, which should look like this:
 
 ```kotlin
     private fun runInference(bitmap: Bitmap) {
@@ -244,6 +244,6 @@ In the example app the CAM annotations would be added to the `runInference()` fu
     }
 ```
 
-The example app is very fast and simple, so the CAMs will not show much, but in a more complex application you could add more CAMs, including child-level ones, to give more detailed annotations to show where time is spent in your application. Indeed for the example app with its very fast inference, you will be best in Streamline to put the timeline view on the 10µs scale to see the CAM annotations better.
+The example application is very fast and simple, so the CAMs will not show much information. In a more complex application you could add more CAMs, including child-level ones, to give more detailed annotations to show where time is spent in your application. For this example app with its very fast inference, it's best to change the Streamline timeline view scale to 10µs in order to see the CAM annotations better.
 
-Once you've added in useful CAM annotations, you can build and deploy a debug version of your application, you can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. Once the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.
+Once you've added in useful CAM annotations, you can build and deploy a debug version of your application. You can run Streamline and see the annotations and CAMs in the timeline view. See the [Streamline documentation](https://developer.arm.com/documentation/101816/latest/) for how to make a capture for profiling. After the capture is made and analyzed, you will be able to see when your application is running the inference, ML pre-processing, ML post-processing, or other parts of your application. From there you can see where the most time is spent, and how hard the CPU or GPU is working during different parts of the application. From this you can then decide if work is needed to improve performance and where that work needs doing.

From 0c8b302bbe418985a1add2a9a05f8dcf475b2000 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 15:17:17 -0500
Subject: [PATCH 13/17] Update nn-profiling-general.md

---
 .../profiling-ml-on-arm/nn-profiling-general.md             | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
index 7ca228759..91a35381f 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-general.md
@@ -9,8 +9,8 @@ layout: learningpathall
 ## Profiling your model
 App profilers will give you a good overall view of your performance, but often you might want to look inside the model and work out bottlenecks within the network. The network is often the bulk of the time, in which case it will warrant closer analysis. 
 
-General profilers are unable to do this, as there needs to be annotations inside the ML framework code to get the information. It is a large task to write the profiling annotations throughout the framework, so it is easier if we use tools from a framework or inference engine that can already do the job.
+With general profilers this is hard to do, as there needs to be annotations inside the ML framework code to get the information. It is a large task to write the profiling annotations throughout the framework, so it is easier to use tools from a framework or inference engine that already has the required instrumentation.
 
-Depending on your model the tools available will differ. For example, if you are using LiteRT (formerly TensorFlow Lite), Arm provides the ArmNN delegate that you can run the model with on Linux or Android, CPU or GPU. ArmNN in turn provides a tool called `ExecuteNetwork` that can run the model and give you layer timings and other useful information.
+Depending on your model, your choice of tools will differ. For example, if you are using LiteRT (formerly TensorFlow Lite), Arm provides the ArmNN delegate that you can run with the model running on Linux or Android, CPU or GPU. ArmNN in turn provides a tool called `ExecuteNetwork` that can run the model and give you layer timings among other useful information.
 
-If you are using PyTorch, you will probably want ExecuTorch on an Android phone, and this also has a profiler available alongside it.
+If you are using PyTorch, you will probably use ExecuTorch the ons-device inference runtime for your Android phone. ExecuTorch has a profiler available alongside it.

From 09d1b819a1cffe3184f16cb379080804e75fcdf2 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 15:45:38 -0500
Subject: [PATCH 14/17] Update nn-profiling-executenetwork.md

---
 .../nn-profiling-executenetwork.md            | 40 +++++++++----------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
index b046364a8..f4ca26994 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/nn-profiling-executenetwork.md
@@ -1,5 +1,5 @@
 ---
-title: ML profiling with ExecuteNetwork
+title: ML profiling of a tflite model with ExecuteNetwork
 weight: 6
 
 ### FIXED, DO NOT MODIFY
@@ -7,39 +7,38 @@ layout: learningpathall
 ---
 
 ## ArmNN's Network Profiler
-One way of running tflite models is with ArmNN. This is available as a delegate to the standard tflite interpreter. But to profile the model, ArmNN comes with a command-line utility called `ExecuteNetwork`. This program just runs the model without the rest of an app. It is able to output layer timings and other useful information to let you know where there might be bottlenecks within your model.
+One way of running tflite models is with ArmNN. This is available as a delegate to the standard tflite interpreter. But to profile the model, ArmNN comes with a command-line utility called `ExecuteNetwork`. This program just runs the model without the rest of the app. It is able to output layer timings and other useful information to let you know where there might be bottlenecks within your model.
 
-If you are not using tflite, you'll need to look at other tools from your framework to profile your model. If you are using tflite without ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to see if there are any obvious problems.
+If you are using tflite without ArmNN, then the output from `ExecuteNetwork` will be more of an indication than a definitive answer. But it can still be useful to spot any obvious problems.
 
-If you want to have a tflite to practice this Learning Path with, you can download one from the [Arm Model Zoo](https://github.com/ARM-software/ML-zoo). As an example we'll use a [mobilenet tflite](https://github.com/ARM-software/ML-zoo/blob/master/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/mobilenet_v2_1.0_224_INT8.tflite).
+To try this out, you can download a tflite model from the [Arm Model Zoo](https://github.com/ARM-software/ML-zoo). In this Learning Path, you will download [mobilenet tflite](https://github.com/ARM-software/ML-zoo/blob/master/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/mobilenet_v2_1.0_224_INT8.tflite).
 
-To get `ExecuteNetwork` you can download it from the [ArmNN GitHub](https://github.com/ARM-software/armnn/releases). Download the version appropriate for the Android phone you wish to test on - the Android version and the architecture of the phone. If you are unsure of the architecture, you can use a lower one, but you may miss out on some optimizations. Inside the tar.gz that you download, `ExecuteNetwork` is included. Note among the other release downloads on the ArmNN Github is the separate file for the `aar` delegate which is the easy way to include the ArmNN delegate into your app.
+To get `ExecuteNetwork` you can download it from the [ArmNN GitHub](https://github.com/ARM-software/armnn/releases). Download the version appropriate for the Android phone you wish to test on - the Android version and the architecture of the phone. If you are unsure of the architecture, you can use a lower one, but you may miss out on some optimizations. Inside the `tar.gz` archive that you download, `ExecuteNetwork` is included. Note among the other release downloads on the ArmNN Github is the separate file for the `aar` delegate which is the easy way to include the ArmNN delegate into your app.
 
-To run `ExecuteNetwork` you'll need to use `adb` to push the model and the executable to your phone, and then run it from the adb shell. `adb` is included with Android Studio, but you may need to add it to your path. Android Studio normally installs it to a location like \<user>\AppData\Local\Android\Sdk\platform-tools. `adb` can also be downloaded separately from the [Android Developer site](https://developer.android.com/studio/releases/platform-tools).
+To run `ExecuteNetwork` you'll need to use `adb` to push the model and the executable to your phone, and then run it from the adb shell. `adb` is included with Android Studio, but you may need to add it to your path. Android Studio normally installs it to a location like `\<user>\AppData\Local\Android\Sdk\platform-tools`. `adb` can also be downloaded separately from the [Android Developer site](https://developer.android.com/studio/releases/platform-tools).
 
-Unzip the tar.gz folder you downloaded to somewhere convenient. From a command prompt, you can then adapt and run the following commands to push the files to your phone. The `/data/local/tmp` folder of your Android device is a place with relaxed permissions that you can use to run this profiling.
+Unzip the `tar.gz` folder you downloaded. From a command prompt, you can then adapt and run the following commands to push the files to your phone. The `/data/local/tmp` folder of your Android device is a place with relaxed permissions that you can use to run this profiling.
 
 ```bash
-adb push mobilenet_v2_1.0_224_INT8.tflite /data/local/tmp/ # if you are using the example tflite model
-# adb push yourmodel.tflite /data/local/tmp/ # where yourmodel is the tflite NN model file that you wish to profile
+adb push mobilenet_v2_1.0_224_INT8.tflite /data/local/tmp/ 
 adb push ExecuteNetwork /data/local/tmp/
 adb push libarm_compute.so /data/local/tmp/
 adb push libarmnn.so /data/local/tmp/
 adb push libarmnn_support_library.so /data/local/tmp/
 # more ArmNN .so library files
 ```
-Push all the `.so` library files that are in the base folder of the tar.gz you downloaded, alongside `ExecuteNetwork`, and all the `.so` files in the `delegate` sub-folder. If you are using a recent version of Android Studio this copying can be done much more easily with drag and drop in the *Device Explorer > Files*.
+Push all the `.so` library files that are in the base folder of the `tar.gz` archive you downloaded, alongside `ExecuteNetwork`, and all the `.so` files in the `delegate` sub-folder. If you are using a recent version of Android Studio this copying can be done much more easily with drag and drop in the *Device Explorer > Files*.
 
 Then you need to set the permissions on the files:
 
 ```bash
-adb shell        # to get into the adb shell on the phone
+adb shell       
 cd /data/local/tmp
-chmod 777 ExecuteNetwork    # to make the file executable
-chmod 777 *.so	   # to make the library files executable
+chmod 777 ExecuteNetwork    
+chmod 777 *.so	   
 ```
 
-Then you can run ExecuteNetwork to profile the model. If you are using the example tflite, you can use the following command:
+Now you can run ExecuteNetwork to profile the model. With the example tflite, you can use the following command:
 
 ```bash
 LD_LIBRARY_PATH=. ./ExecuteNetwork -m mobilenet_v2_1.0_224_INT8.tflite -c CpuAcc -T delegate --iterations 2 --do-not-print-output --enable-fast-math --fp16-turbo-mode -e --output-network-details > modelout.txt
@@ -49,12 +48,12 @@ If you are using your own tflite, replace `mobilenet_v2_1.0_224_INT8.tflite` wit
 
 This will run the model twice, outputting the layer timings to `modelout.txt`. The `--iterations 2` flag is the command that means it runs twice: the first run includes a lot of startup costs and one-off optimizations, so the second run is more indicative of the real performance.
 
-For the other flags, of note are the `-e` and `--output-network-details` flags which will output a lot of timeline information about the model, including the layer timings. The `--do-not-print-output` flag will stop the output of the model, which can be very large, and without sensible input, meaningless. The `--enable-fast-math` and `--fp16-turbo-mode` flags enable some maths optimizations. `CpuAcc` is the acclerated CPU backend, it can be replaced with `GpuAcc` for the accelerated GPU backend. 
+The other flags to note are the `-e` and `--output-network-details` flags which will output a lot of timeline information about the model, including the layer timings. The `--do-not-print-output` flag will stop the output of the model, which can be very large, and without sensible input it is meaningless. The `--enable-fast-math` and `--fp16-turbo-mode` flags enable some math optimizations. `CpuAcc` is the acclerated CPU backend, it can be replaced with `GpuAcc` for the accelerated GPU backend. 
 
-After running the model, you can pull the output file back to your computer with the following commands:
+After running the model, you can pull the output file back to your host machine with the following commands:
 
 ```bash
-exit         # to leave the adb shell
+exit        
 adb pull /data/local/tmp/modelout.txt
 ```
 Once again, this can be done with drag and drop in Android Studio's *Device Explorer > Files*.
@@ -62,6 +61,7 @@ Once again, this can be done with drag and drop in Android Studio's *Device Expl
 Depending on the size of your model, the output will probably be quite large. You can use a text editor to view the file. The output is in JSON format, so you can use a JSON viewer to make it more readable. Usually some scripting can be used to extract the information you need more easily out of the very raw data in the file.
 
 At the top is the summary, with the setup time and inference time of your 2 runs, which will look something like this:
+
 ```text
 Info: ArmNN v33.2.0
 Info: Initialization time: 7.20 ms.
@@ -78,8 +78,8 @@ Info: Execution time: 468.42 ms.
 Info: Inference time: 468.58 ms
 ```
 
-After the summary comes the graph of the model, and then the layers and their timings from the second run. At the start of the layers there are a few optimizations and their timings recorded before the network itself, so you can skip past the graph and the optimization timings to get to the part that wants analyzing.  
+After the summary comes the graph of the model, then the layers and their timings from the second run. At the start of the layers there are a few optimizations and their timings recorded before the network itself. You can skip past the graph and the optimization timings to get to the part that needs analyzing.  
 
-In the mobilenet example output, the graph is from lines 18 to 1629. After this come the optimization timings, which are part of the runtime, but not the network - these go until line 1989. Next there are a few wall clock recordings for the loading of the network, before the first layer "Convolution2dLayer_CreateWorkload_#18" at line 2036. Here is where the layer info that wants analyzing starts.
+In the mobilenet example output, the graph is from lines 18 to 1629. After this is the optimization timings, which are part of the runtime, but not the network - these go until line 1989. Next there are a few wall clock recordings for the loading of the network, before the first layer "Convolution2dLayer_CreateWorkload_#18" at line 2036. Here is where the layer info that needs analyzing starts.
 
-The layers' "Wall clock time" in microseconds shows how long they took to run. These layers and their timings can then be analyzed to see which layers, and which operators, took the most time.
\ No newline at end of file
+The layers' "Wall clock time" in microseconds shows how long they took to run. These layers and their timings can then be analyzed to see which layers, and which operators, took the most time.

From 73461abce4dcab18f35759b6ca283726b6880697 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 15:57:55 -0500
Subject: [PATCH 15/17] Update app-profiling-android-studio.md

---
 .../app-profiling-android-studio.md                    | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
index 7d49a7303..9f8508f3a 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/app-profiling-android-studio.md
@@ -13,7 +13,7 @@ To find the Profiler, open your project in Android Studio and click on the *View
 
 Most likely with an Android ML app you'll need to look at memory both from the Java/Kotlin side and the native side. The Java/Kotlin side is where the app runs, and may be where buffers are allocated for input and output if, for example, you're using LiteRT (formerly known as TensorFlow Lite). The native side is where the ML framework will run. Looking at the memory consumption for Java/Kotlin and native is 2 separate tasks in the Profiler: *Track Memory Consumption (Java/Kotlin Allocations)* and *Track Memory Consumption (Native Allocations)*.
 
-Before we start either task we want to build our profileable app. The instructions for this and for general profiling setup can be found [here](https://developer.android.com/studio/profile). We will want to start the correct profiling version of the app depending on the task.
+Before you start either task, you have to build your app for profiling. The instructions for this and for general profiling setup can be found [here](https://developer.android.com/studio/profile). You will want to start the correct profiling version of the app depending on the task.
 
 ![Android Studio profiling run types alt-text#center](android-profiling-version.png "Figure 1. Profiling run versions")
 
@@ -21,14 +21,14 @@ For the Java/Kotlin side, you want the **debuggable** "Profile 'app' with comple
 
 ### Java/Kotlin
 
-If we start looking at the [Java/Kotlin side](https://developer.android.com/studio/profile/record-java-kotlin-allocations), choose *Profiler: Run 'app' as debuggable*, and then select the *Track Memory Consumption (Java/Kotlin Allocations)* task. Navigate to the part of the app you wish to profile and then we can start profiling. At the bottom of the Profiling window it should look like Figure 2 below. Click *Start Profiler Task*.
+If you start looking at the [Java/Kotlin side](https://developer.android.com/studio/profile/record-java-kotlin-allocations), choose *Profiler: Run 'app' as debuggable*, and then select the *Track Memory Consumption (Java/Kotlin Allocations)* task. Navigate to the part of the app you wish to profile and then you can start profiling. At the bottom of the Profiling window it should look like Figure 2 below. Click *Start Profiler Task*.
 
 ![Android Studio Start Profile alt-text#center](start-profile-dropdown.png "Figure 2. Start Profile")
 
-When you're ready, *Stop* the profiling again. Now there'll be a nice timeline graph of memory usage. Unfortunately, while Android Studio has a nicer interface for the Java/Kotlin side than the native side, the key to the timeline graph may be missing. This key is shown below in Figure 3, so you can refer to the colors from this.
+When you're ready, *Stop* the profiling again. Now there will be a nice timeline graph of memory usage. While Android Studio has a nicer interface for the Java/Kotlin side than the native side, the key to the timeline graph may be missing. This key is shown below in Figure 3, so you can refer to the colors from this.
 ![Android Studio memory key alt-text#center](profiler-jk-allocations-legend.png "Figure 3. Memory key for the Java/Kotlin Memory Timeline")
 
-The default height of the Profiling view - as well as the timeline graph within it - is usually too small, so adjust these heights to get a sensible graph. You can click at different points of the graph to see the memory allocations at that time. If you look according to the key you can see how much memory is allocated by Java, Native, Graphics, Code etc.
+The default height of the Profiling view, as well as the timeline graph within it is usually too small, so adjust these heights to get a sensible graph. You can click at different points of the graph to see the memory allocations at that time. If you look according to the key you can see how much memory is allocated by Java, Native, Graphics, Code etc.
 
 Looking further down you can see the *Table* of Java/Kotlin allocations for your selected time on the timeline. With ML a lot of your allocations are likely to be byte[] for byte buffers, or possibly int[] for image data, etc. Clicking on the data type will open up the particular allocations, showing their size and when they were allocated. This will help to quickly narrow down their use, and whether they are all needed etc.
 
@@ -36,7 +36,7 @@ Looking further down you can see the *Table* of Java/Kotlin allocations for your
 
 For the [native side](https://developer.android.com/studio/profile/record-native-allocations), the process is similar but with different options. Choose *Profiler: Run 'app' as profileable*, and then select the *Track Memory Consumption (Native Allocations)* task. Here you have to *Start profiler task from: Process Start*. Choose *Stop* once you've captured enough data.
 
-The Native view doesn't have the same nice timeline graph as the Java/Kotlin side, but it does have the *Table* and *Visualization* tabs. The *Table* tab no longer has a list of allocations, but options to *Arrange by allocation method* or *callstack*. Choose *Arrange by callstack* and then you can trace down which functions were allocating signifcant memory. Potentially more useful, you can also see Remaining Size. 
+The Native view doesn't have the same nice timeline graph as the Java/Kotlin side, but it does have the *Table* and *Visualization* tabs. The *Table* tab no longer has a list of allocations, but options to *Arrange by allocation method* or *callstack*. Choose *Arrange by callstack* and then you can trace down which functions were allocating significant memory. Potentially more useful, you can also see Remaining Size. 
 
 In the Visualization tab you can see the callstack as a graph, and once again you can look at total Allocations Size or Remaining Size. If you look at Remaining Size, you can see what is still allocated at the end of the profiling, and by looking a few steps up the stack, probably see which allocations are related to the ML model, by seeing functions that relate to the framework you are using. A lot of the memory may be allocated by that framework rather than in your code, and you may not have much control over it, but it is useful to know where the memory is going.
 

From 46c451bbc9543de98759ecd8db8d87a690217a31 Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 16:03:49 -0500
Subject: [PATCH 16/17] Update _review.md

---
 .../smartphones-and-mobile/profiling-ml-on-arm/_review.md     | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
index a1f46d1ed..7eae5a8b1 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_review.md
@@ -16,8 +16,8 @@ review:
         question: >
             Does Android Studio have a profiler?
         answers:
-            - Yes
-            - No
+            - "Yes"
+            - "No"
         correct_answer: 1                   
         explanation: >
             Yes, Android Studio has a built-in profiler that can be used to monitor the memory usage of your app among other things

From fc1a47d07e157058499d528eb6fa85ca267708bd Mon Sep 17 00:00:00 2001
From: pareenaverma <pareena.verma@arm.com>
Date: Thu, 14 Nov 2024 16:07:53 -0500
Subject: [PATCH 17/17] Update _next-steps.md

---
 .../profiling-ml-on-arm/_next-steps.md                       | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
index aafea4126..f468cb1b8 100644
--- a/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
+++ b/content/learning-paths/smartphones-and-mobile/profiling-ml-on-arm/_next-steps.md
@@ -1,8 +1,7 @@
 ---
-next_step_guidance: Continue Learning
+next_step_guidance: You might be interested in learning how to profile your Unity apps on Android.
 
-recommended_path: /learning-paths/smartphones-and-mobile/build-llama3-chat-android-app-using-executorch-and-xnnpack/
-recommended_path: /learning-paths/smartphones-and-mobile/kleidiai-on-android-with-mediapipe-and-xnnpack/
+recommended_path: /learning-paths/smartphones-and-mobile/profiling-unity-apps-on-android/
 
 further_reading:
     - resource: