diff --git a/ethosu/vela/operation_util.py b/ethosu/vela/operation_util.py
new file mode 100644
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+++ b/ethosu/vela/operation_util.py
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+# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the License); you may
+# not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an AS IS BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Description:
+# Utility functions for creating Network Operations.
+from typing import Optional
+
+from .data_type import DataType
+from .high_level_command_to_npu_op import ifm_ifm2_correct_order
+from .operation import ActivationFunction
+from .operation import Op
+from .operation import Operation
+from .tensor import create_reshape_tensor
+from .tensor import QuantizationParameters
+from .tensor import Tensor
+
+
+def create_avgpool_nop(name: str) -> Operation:
+    op = Operation(Op.AvgPool, name)
+    op.attrs["padding"] = b"VALID"
+    op.attrs["stride_w"] = 1
+    op.attrs["stride_h"] = 1
+    op.attrs["filter_width"] = 1
+    op.attrs["filter_height"] = 1
+    op.attrs["strides"] = [1, 1, 1, 1]
+    op.attrs["ksize"] = [1, 1, 1, 1]
+    op.attrs["skirt"] = [0, 0, 0, 0]
+    op.attrs["explicit_padding"] = [0, 0, 0, 0]
+    return op
+
+
+def create_depthwise_maxpool(
+    name: str, ifm: Tensor, quantization: QuantizationParameters, activation: Optional[ActivationFunction] = None
+) -> Operation:
+    op = Operation(Op.MaxPool, name)
+    height = ifm.shape[1] * ifm.shape[2]
+    width = ifm.shape[3]
+    ifm_shape = [1, height, width, 1]
+    op.attrs["padding"] = b"VALID"
+    op.attrs["stride_w"] = 1
+    op.attrs["stride_h"] = 1
+    op.attrs["filter_width"] = width
+    op.attrs["filter_height"] = 1
+    op.attrs["strides"] = [1, op.attrs["stride_h"], op.attrs["stride_w"], 1]
+    op.attrs["ksize"] = [1, op.attrs["filter_height"], op.attrs["filter_width"], 1]
+    op.activation = activation
+    op.inputs = [create_reshape_tensor(ifm, ifm_shape)]
+    ofm = Tensor([1, height, 1, 1], ifm.dtype, op.name + "_tens0")
+    ofm.quantization = quantization
+    op.set_output_tensor(ofm)
+    return op
+
+
+def create_reduce_sum(
+    name: str, ifm: Tensor, quantization: QuantizationParameters, activation: Optional[ActivationFunction] = None
+) -> Operation:
+    op = Operation(Op.ReduceSum, name)
+    op.attrs["padding"] = b"VALID"
+    op.attrs["stride_w"] = 1
+    op.attrs["stride_h"] = 1
+    op.attrs["filter_width"] = 1
+    op.attrs["filter_height"] = 1
+    op.attrs["strides"] = [1, op.attrs["stride_h"], op.attrs["stride_w"], 1]
+    op.attrs["ksize"] = [1, op.attrs["filter_height"], op.attrs["filter_width"], 1]
+    op.add_input_tensor(ifm)
+    op.activation = activation
+    ofm_shape = [1, ifm.shape[1], ifm.shape[2], 1]
+    sum_of_exp = Tensor(ofm_shape, DataType.int32, op.name + "_tens0")
+    sum_of_exp.quantization = quantization
+    op.set_output_tensor(sum_of_exp)
+    return op
+
+
+def create_add(
+    name: str,
+    ifm: Tensor,
+    ifm2: Tensor,
+    quantization: QuantizationParameters,
+    activation: Optional[ActivationFunction] = None,
+    dtype: Optional[DataType] = None,
+    attrs: Optional[dict] = None,
+) -> Operation:
+    return create_binary_elementwise(Op.Add, name, ifm, ifm2, quantization, activation, dtype, attrs)
+
+
+def create_clz(
+    name: str,
+    ifm: Tensor,
+    quantization: QuantizationParameters,
+    activation: Optional[ActivationFunction] = None,
+    dtype: Optional[DataType] = None,
+    attrs: Optional[dict] = None,
+) -> Operation:
+    return create_unary_elementwise(Op.CLZ, name, ifm, quantization, activation, dtype, attrs)
+
+
+def create_mul(
+    name: str,
+    ifm: Tensor,
+    ifm2: Tensor,
+    quantization: QuantizationParameters,
+    activation: Optional[ActivationFunction] = None,
+    dtype: Optional[DataType] = None,
+    attrs: Optional[dict] = None,
+) -> Operation:
+    return create_binary_elementwise(Op.Mul, name, ifm, ifm2, quantization, activation, dtype, attrs)
+
+
+def create_shl(
+    name: str,
+    ifm: Tensor,
+    ifm2: Tensor,
+    quantization: QuantizationParameters,
+    activation: Optional[ActivationFunction] = None,
+    dtype: Optional[DataType] = None,
+    attrs: Optional[dict] = None,
+) -> Operation:
+    return create_binary_elementwise(Op.SHL, name, ifm, ifm2, quantization, activation, dtype, attrs)
+
+
+def create_shr(
+    name: str,
+    ifm: Tensor,
+    ifm2: Tensor,
+    quantization: QuantizationParameters,
+    activation: Optional[ActivationFunction] = None,
+    dtype: Optional[DataType] = None,
+    attrs: Optional[dict] = None,
+) -> Operation:
+    return create_binary_elementwise(Op.SHR, name, ifm, ifm2, quantization, activation, dtype, attrs)
+
+
+def create_sub(
+    name: str,
+    ifm: Tensor,
+    ifm2: Tensor,
+    quantization: QuantizationParameters,
+    activation: Optional[ActivationFunction] = None,
+    dtype: Optional[DataType] = None,
+    attrs: Optional[dict] = None,
+) -> Operation:
+    return create_binary_elementwise(Op.Sub, name, ifm, ifm2, quantization, activation, dtype, attrs)
+
+
+def create_unary_elementwise(
+    op_type: Op,
+    name: str,
+    ifm: Tensor,
+    quantization: QuantizationParameters,
+    activation: Optional[ActivationFunction] = None,
+    dtype: Optional[DataType] = None,
+    attrs: Optional[dict] = None,
+) -> Operation:
+    return create_binary_elementwise(op_type, name, ifm, None, quantization, activation, dtype, attrs)
+
+
+def create_binary_elementwise(
+    op_type: Op,
+    name: str,
+    ifm: Tensor,
+    ifm2: Tensor,
+    quantization: QuantizationParameters,
+    activation: Optional[ActivationFunction] = None,
+    dtype: Optional[DataType] = None,
+    attrs: Optional[dict] = None,
+) -> Operation:
+    op = Operation(op_type, name)
+    op.add_input_tensor(ifm)
+    if ifm2:
+        op.add_input_tensor(ifm2)
+    op.activation = activation
+    if not dtype:
+        dtype = ifm.dtype
+    if attrs:
+        op.attrs.update(attrs)
+    ofm_shape = ifm.shape if ifm2 is None or ifm_ifm2_correct_order(ifm.shape, ifm2.shape) else ifm2.shape
+    ofm = Tensor(ofm_shape, dtype, f"{op.name}_tens0")
+    ofm.quantization = quantization
+    op.set_output_tensor(ofm)
+    return op