From f3c46b09f9d04904a045ce5fbe7c9ae29f3f82b2 Mon Sep 17 00:00:00 2001 From: sqPoseidon Date: Wed, 17 Jul 2024 11:01:59 +0800 Subject: [PATCH] update --- publication.html | 3 +++ research.html | 11 +++++++---- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/publication.html b/publication.html index dfbf2b8..67bf327 100755 --- a/publication.html +++ b/publication.html @@ -52,8 +52,11 @@

Publications

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* denotes equal contribution, # denotes corresponding author.

Conference papers

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  1. Xudong Lu, Yuqi Jiang, Haiwen Hong, Qi Sun#, Cheng Zhuo#, ‘‘DCAFuse: Dual-Branch Diffusion-CNN Complementary Feature Aggregation Network for Multi-Modality Image Fusion’’, ACM International Conference on Multimedia (MM), Melbourne, Australia, Oct. 28-Nov. 01, 2024.

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  2. Lvcheng Chen, Ying Wu, Chenyi Wen, Shizhang Wang, Li Zhang, Bei Yu, Qi Sun#, Cheng Zhuo#,‘‘An Agile Framework for Efficient LLM Accelerator Development and Model Inference’’, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), New Jersey, Oct. 27–31, 2024.

  3. Qian Jin, Yuqi Jiang, Xudong Lu, Yumeng Liu, Yining Chen, Dawei Gao, Qi Sun#, Cheng Zhuo#,‘‘SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image’’, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), New Jersey, Oct. 27–31, 2024.

    diff --git a/research.html b/research.html index c060e56..34fb03d 100755 --- a/research.html +++ b/research.html @@ -52,15 +52,16 @@

    Research

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    Machine Learning for EDA

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    As the technology node of integrated circuits rapidly scales down to 5nm and beyond, the electronic design automation (EDA) in Very Large Scale Integration (VLSI) which has been developed over the last few decades, is challenged by the ever-increasing VLSI design complexity. Machine learning has shown great potential in various fields, including EDA. Our major achievements are proposing and customizing machine learning techniques in many EDA applications. Our research topics include design optimization, performance modeling, co-optimization, IC manufacturing, etc. +

    AI (Large Language Model) for EDA

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    As the technology node of integrated circuits rapidly scales down to 5nm and beyond, the electronic design automation (EDA) in Very Large Scale Integration (VLSI) which has been developed over the last few decades, is challenged by the ever-increasing VLSI design complexity. Artificial Intelligence has shown great potential in various fields, including EDA. Our major achievements are proposing and customizing machine learning and artificial intelligence techniques, especially the emerging large language model techniques, in many EDA applications. Our research topics include design optimization, performance modeling, co-optimization, IC manufacturing, etc. Selected publications:

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    • Yuqi Jiang, Qian Jin, Xudong Lu, Qi Sun#, Cheng Zhuo, “FabSage: A Large Multimodal Model for IC Defect Detection, Analysis, and Knowledge Querying -”, 1st IEEE International Workshop on LLM-Aided Design, San Jose, CA, June 28-29, 2024.

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    • Qian Jin, Yuqi Jiang, Xudong Lu, Yumeng Liu, Yining Chen, Dawei Gao, Qi Sun#, Cheng Zhuo#,“SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), New Jersey, Oct. 27–31, 2024.

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    • Yuqi Jiang, Xudong Lu, Qian Jin, Qi Sun#, Hanming Wu, Cheng Zhuo#, “FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), New Jersey, Oct. 27–31, 2024. (arxiV)

    • Donger Luo*, Qi Sun*, Xinheng Li, Chen Bai, Bei Yu, Hao Geng, “Knowing The Spec to Explore The Design via Transformed Bayesian Optimization”, ACM/IEEE Design Automation Conference (DAC), San Francisco, Jun. 23–27, 2024.

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      Machine Learning for EDA

      Deep Learning Algorithms

      Deep learning algorithms have driven progress in many fields, and with the development of large language models, they have revolutionized traditional development paradigms. We are exploring improvements and advancements in deep learning and large models. Selected publications:

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      • Xudong Lu, Yuqi Jiang, Haiwen Hong, Qi Sun#, Cheng Zhuo#, “DCAFuse: Dual-Branch Diffusion-CNN Complementary Feature Aggregation Network for Multi-Modality Image Fusion”, ACM International Conference on Multimedia (MM), Melbourne, Australia, Oct. 28-Nov. 01, 2024.

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      • Xianting Lu, Yunong Wang, Yu Fu, Qi Sun, Xuhua Ma, Xudong Zheng, Cheng Zhuo, “MISP: A Multimodal-based Intelligent Server Failure Prediction Model for Cloud Computing Systems”, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Barcelona, Spain, Aug. 25-29, 2024.

      • Yuqi Jiang, Qian Jin, Xudong Lu, Qi Sun#, Cheng Zhuo, “FabSage: A Large Multimodal Model for IC Defect Detection, Analysis, and Knowledge Querying”, IEEE International Workshop on LLM-Aided Design (LAD), San Jose, Jun. 28-29, 2024.