diff --git a/backends/arm/operators/op_avg_pool2d.py b/backends/arm/operators/op_avg_pool2d.py
index d84fe40d99..e6d07610c8 100644
--- a/backends/arm/operators/op_avg_pool2d.py
+++ b/backends/arm/operators/op_avg_pool2d.py
@@ -10,8 +10,8 @@
     NodeVisitor,
     register_node_visitor,
 )
-from executorch.backends.arm.operators.op_common import build_avg_pool_2d_common
 from executorch.backends.arm.tosa_mapping import TosaArg
+from executorch.backends.arm.tosa_utils import build_avg_pool_2d_common
 
 
 @register_node_visitor
diff --git a/backends/arm/operators/op_common.py b/backends/arm/operators/op_common.py
deleted file mode 100644
index eadf00c294..0000000000
--- a/backends/arm/operators/op_common.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright 2024 Arm Limited and/or its affiliates.
-#
-# This source code is licensed under the BSD-style license found in the
-# LICENSE file in the root directory of this source tree.
-
-import serializer.tosa_serializer as ts
-import torch
-from executorch.backends.arm.tosa_mapping import TosaArg
-from executorch.backends.arm.tosa_quant_utils import get_quant_node_args
-from serializer.tosa_serializer import TosaOp
-
-
-def build_avg_pool_2d_common(
-    node: torch.fx.Node,
-    tosa_graph: ts.TosaSerializer,
-    input_tensor: TosaArg,
-    kernel_size: list,
-    stride: list,
-    padding: list,
-    is_quant_node: bool,
-    output: TosaArg,
-):
-    accumulator_type = input_tensor.dtype
-
-    if is_quant_node:
-        # Accumulator type always is int32 when input tensor is an integer type.
-        accumulator_type = ts.DType.INT32
-
-    # Initilize zero point to zero.
-    input_zp = 0
-    output_zp = 0
-
-    if is_quant_node:
-        input_zp = get_quant_node_args(node.args[0]).zp
-        output_zp = get_quant_node_args(list(node.users)[0]).zp
-
-    attr = ts.TosaSerializerAttribute()
-    attr.PoolAttribute(
-        kernel=kernel_size,
-        stride=stride,
-        pad=padding,
-        input_zp=input_zp,
-        output_zp=output_zp,
-        accum_dtype=accumulator_type,
-    )
-
-    tosa_graph.addOperator(
-        TosaOp.Op().AVG_POOL2D,
-        [input_tensor.name],
-        [output.name],
-        attr,
-    )
diff --git a/backends/arm/operators/op_mean_dim.py b/backends/arm/operators/op_mean_dim.py
index 5e8e3d74c0..20e1b2b8d7 100644
--- a/backends/arm/operators/op_mean_dim.py
+++ b/backends/arm/operators/op_mean_dim.py
@@ -10,8 +10,8 @@
     NodeVisitor,
     register_node_visitor,
 )
-from executorch.backends.arm.operators.op_common import build_avg_pool_2d_common
 from executorch.backends.arm.tosa_mapping import TosaArg
+from executorch.backends.arm.tosa_utils import build_avg_pool_2d_common
 
 
 @register_node_visitor
diff --git a/backends/arm/tosa_utils.py b/backends/arm/tosa_utils.py
index 68d090653a..a692b3a270 100644
--- a/backends/arm/tosa_utils.py
+++ b/backends/arm/tosa_utils.py
@@ -6,11 +6,12 @@
 import logging
 import os
 
-import executorch.backends.arm.tosa_quant_utils as tosa_quant_utils
-
 import numpy as np
 import serializer.tosa_serializer as ts
+import torch
 from executorch.backends.arm.tosa_mapping import TosaArg
+
+from executorch.backends.arm.tosa_quant_utils import get_quant_node_args, q_op
 from executorch.exir.dialects._ops import ops as exir_ops
 from serializer.tosa_serializer import TosaOp
 
@@ -158,7 +159,7 @@ def is_bias_node_for_addmm(node):
     # consumer node is addmm
     is_rank2_linear_bias = (
         consumer_node.target == exir_ops.edge.aten.addmm.default
-        and list(consumer_node.users)[0].target == tosa_quant_utils.q_op
+        and list(consumer_node.users)[0].target == q_op
     )
 
     # rank>2 linear layers
@@ -170,7 +171,7 @@ def is_bias_node_for_addmm(node):
     ):
         consumer_consumer_node = list(consumer_node.users)[0]
         is_rank_greater_than_2_linear_bias = (
-            list(consumer_consumer_node.users)[0].target == tosa_quant_utils.q_op
+            list(consumer_consumer_node.users)[0].target == q_op
         )
 
     return is_rank2_linear_bias or is_rank_greater_than_2_linear_bias
@@ -189,3 +190,45 @@ def is_consumer_node_depthwise_conv2d(node):
             return True
 
     return False
+
+
+def build_avg_pool_2d_common(
+    node: torch.fx.Node,
+    tosa_graph: ts.TosaSerializer,
+    input_tensor: TosaArg,
+    kernel_size: list,
+    stride: list,
+    padding: list,
+    is_quant_node: bool,
+    output: TosaArg,
+):
+    accumulator_type = input_tensor.dtype
+
+    if is_quant_node:
+        # Accumulator type always is int32 when input tensor is an integer type.
+        accumulator_type = ts.DType.INT32
+
+    # Initilize zero point to zero.
+    input_zp = 0
+    output_zp = 0
+
+    if is_quant_node:
+        input_zp = get_quant_node_args(node.args[0]).zp
+        output_zp = get_quant_node_args(list(node.users)[0]).zp
+
+    attr = ts.TosaSerializerAttribute()
+    attr.PoolAttribute(
+        kernel=kernel_size,
+        stride=stride,
+        pad=padding,
+        input_zp=input_zp,
+        output_zp=output_zp,
+        accum_dtype=accumulator_type,
+    )
+
+    tosa_graph.addOperator(
+        TosaOp.Op().AVG_POOL2D,
+        [input_tensor.name],
+        [output.name],
+        attr,
+    )