diff --git a/tests/sweep_framework/sweeps/eltwise/unary/prelu/prelu.py b/tests/sweep_framework/sweeps/eltwise/unary/prelu/prelu.py
index 134825bdf04..b2940fcf817 100644
--- a/tests/sweep_framework/sweeps/eltwise/unary/prelu/prelu.py
+++ b/tests/sweep_framework/sweeps/eltwise/unary/prelu/prelu.py
@@ -14,11 +14,6 @@
 from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
 from models.utility_functions import torch_random
 
-# Override the default timeout in seconds for hang detection.
-TIMEOUT = 30
-
-random.seed(0)
-
 
 # Parameters provided to the test vector generator are defined here.
 # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
@@ -45,12 +40,6 @@ def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
     return False, None
 
 
-def torch_prelu(x, *args, **kwargs):
-    weight = kwargs.pop("scalar")
-    result = torch.nn.functional.prelu(x, torch.tensor(weight, dtype=x.dtype))
-    return result
-
-
 # This is the run instructions for the test, defined by the developer.
 # The run function must take the above-defined parameters as inputs.
 # The runner will call this run function with each test vector, and the returned results from this function will be stored.
@@ -65,14 +54,14 @@ def run(
     *,
     device,
 ) -> list:
-    data_seed = random.randint(0, 20000000)
-    torch.manual_seed(data_seed)
+    torch.manual_seed(0)
 
     torch_input_tensor_a = gen_func_with_cast_tt(
         partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
     )(input_shape)
 
-    torch_output_tensor = torch_prelu(torch_input_tensor_a, scalar=weight)
+    golden_function = ttnn.get_golden_function(ttnn.prelu)
+    torch_output_tensor = golden_function(torch_input_tensor_a, weight)
 
     input_tensor_a = ttnn.from_torch(
         torch_input_tensor_a,
diff --git a/tests/tt_eager/python_api_testing/sweep_tests/pytests/tt_dnn/test_eltwise_unary.py b/tests/tt_eager/python_api_testing/sweep_tests/pytests/tt_dnn/test_eltwise_unary.py
index ea9029a7cf6..1db66f53ced 100644
--- a/tests/tt_eager/python_api_testing/sweep_tests/pytests/tt_dnn/test_eltwise_unary.py
+++ b/tests/tt_eager/python_api_testing/sweep_tests/pytests/tt_dnn/test_eltwise_unary.py
@@ -871,6 +871,7 @@ def test_run_eltwise_leaky_relu_op(
         )
 
     @pytest.mark.parametrize("weight", [-0.5, 1.0, 0.5])
+    @skip_for_grayskull()
     def test_run_eltwise_prelu(
         self,
         input_shapes,
diff --git a/tests/ttnn/unit_tests/operations/eltwise/test_activation.py b/tests/ttnn/unit_tests/operations/eltwise/test_activation.py
index 57f8ecf4284..4407dd2a306 100644
--- a/tests/ttnn/unit_tests/operations/eltwise/test_activation.py
+++ b/tests/ttnn/unit_tests/operations/eltwise/test_activation.py
@@ -307,6 +307,7 @@ def test_scalarB_leaky_relu(device, h, w, scalar):
     run_activation_test_leaky_relu(device, h, w, scalar, ttnn.leaky_relu)
 
 
+@skip_for_grayskull()
 @pytest.mark.parametrize("weight", [-0.5, 1.0, 0.5])
 @pytest.mark.parametrize("h", [64])
 @pytest.mark.parametrize("w", [128])
diff --git a/tests/ttnn/unit_tests/operations/eltwise/test_binary_composite.py b/tests/ttnn/unit_tests/operations/eltwise/test_binary_composite.py
index 96050f92511..6f5ff325837 100644
--- a/tests/ttnn/unit_tests/operations/eltwise/test_binary_composite.py
+++ b/tests/ttnn/unit_tests/operations/eltwise/test_binary_composite.py
@@ -996,6 +996,7 @@ def test_binary_prelu_ttnn(input_shapes, device):
     "scalar",
     {-0.25, -2.7, 0.45, 6.4},
 )
+@skip_for_grayskull()
 def test_binary_prelu_scalar_ttnn(input_shapes, scalar, device):
     in_data1, input_tensor1 = data_gen_with_range(input_shapes, -100, 100, device)
     output_tensor = ttnn.prelu(input_tensor1, scalar)