diff --git a/ethosu/vela/test/test_tflite_supported_operators.py b/ethosu/vela/test/test_tflite_supported_operators.py
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+++ b/ethosu/vela/test/test_tflite_supported_operators.py
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+# Copyright (C) 2020-2021 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:
+# Unit tests for tflite support_operators
+import numpy as np
+
+from ethosu.vela.data_type import DataType
+from ethosu.vela.operation import ActivationFunction
+from ethosu.vela.operation import Op
+from ethosu.vela.operation import Padding
+from ethosu.vela.tensor import create_const_tensor
+from ethosu.vela.tensor import QuantizationParameters
+from ethosu.vela.tensor import Tensor
+from ethosu.vela.test import testutil
+from ethosu.vela.tflite_supported_operators import TFLiteSupportedOperators
+
+support = TFLiteSupportedOperators()
+
+
+def test_constraint_tens_dtype():
+    # Tensors can only be of type uint8, int8, int16 and int32
+    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.float32)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_tens_int32_ops():
+    # For int32, only select op types are allowed:
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8], datatype=DataType.int32)
+    assert support.is_operator_supported(op)
+    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_tens_dimension():
+    # Tensors can only have values in the inclusive range of 1-65535
+    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 0], [1, 8, 8, 65536])
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_tens_quant_per_axis_not_supp():
+    # Quantization scale cannot be array-valued for elemwise ops
+    qp = QuantizationParameters()
+    qp.zero_point = np.zeros((1, 3))
+    qp.scale_f32 = np.ones((1, 3))
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm_quant=qp)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_tens_quant_per_axis_is_supp():
+    op = testutil.create_op_with_quant_tensors(
+        Op.Conv2DBias, [1, 1, 1, 3], [1, 1, 1, 3], weights_shape=[1, 1, 1, 3], bias_shape=[1, 1, 1, 3]
+    )
+    op.attrs = {"stride_w": 1, "stride_h": 1}
+    assert support.is_operator_supported(op)
+    qp = QuantizationParameters()
+    qp.zero_point = np.zeros((1, 3))
+    qp.scale_f32 = np.ones((1, 3))
+    op.bias.quantization = qp
+    assert support.is_operator_supported(op)
+
+
+def test_constraint_fc_output_2d_is_supp():
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [4, 8, 8, 4], [32, 32], weights_shape=[4, 8, 8, 4])
+    assert support.is_operator_supported(op)
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [1, 1024], [16, 64], weights_shape=[1, 1024])
+    assert support.is_operator_supported(op)
+
+
+def test_constraint_faf():
+    # Fused activation functions, if set, must be a valid op type
+    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.activation = ActivationFunction(Op.Conv2D)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_faf_ofm_dtype():
+    # If fused activation function is present, OFM must be 8 or 16 bit
+    shp = [1, 8, 8, 8]
+    for dtype in [DataType.int8, DataType.uint8, DataType.int16, DataType.int32]:
+        op = testutil.create_elemwise_op(Op.Add, "op", shp, shp, shp, datatype=dtype)
+        op.activation = ActivationFunction(Op.Relu)
+        expected = dtype.size_in_bytes() <= 2
+        assert support.is_operator_supported(op) == expected, f"Data type: {dtype}"
+
+
+def test_constraint_conv_pass():
+    # First test a simple conv passes
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1])
+    op.attrs = {"stride_w": 1, "stride_h": 1}
+    assert support.is_operator_supported(op)
+
+
+def test_constraint_stride_range():
+    # Stride width and height must lie within a certain range
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 0, "stride_h": 20}
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_dilation_range():
+    # Dilation width and height must lie within a certain range
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 1, "stride_h": 1, "dilation_w_factor": 0, "dilation_h_factor": 20}
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_dilated_height_range():
+    # Dilated kernel height must lie within a certain range
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[65, 64, 1, 1])
+    op.attrs = {"stride_w": 1, "stride_h": 1}
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_dilated_product_range():
+    # Dilated kernel width x height must lie within a certain range
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[64, 65, 1, 1])
+    op.attrs = {"stride_w": 1, "stride_h": 1}
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_weights_type():
+    # Weight tensor must be 8-bit
+    op = testutil.create_op_with_quant_tensors(
+        Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1], datatype=DataType.int16
+    )
+    op.attrs = {"stride_w": 1, "stride_h": 1}
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_weights_const():
+    # Weight tensor cannot be non-const tensors
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 1, "stride_h": 1}
+    weights = Tensor([64, 64, 1, 1], DataType.uint8, "weights")
+    weights.quantization = testutil.default_quant_params()
+    op.add_input_tensor(weights)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_weights_limit():
+    # Sum of weights has a limit
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1])
+    op.attrs = {"stride_w": 1, "stride_h": 1}
+    op.weights.quantization.zero_point = np.array([[[[(127 * 65536) + 1]]]])
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_bias_type():
+    # Bias must have a certain datatype
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1])
+    op.attrs = {"stride_w": 1, "stride_h": 1}
+    bias = Tensor([1, 8, 8, 8], DataType.uint8, "bias")
+    op.add_input_tensor(bias)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_bias_40bit():
+    # Bias must not exceed 40-bit
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1])
+    op.attrs = {"stride_w": 1, "stride_h": 1}
+    bias = Tensor([1, 1, 1, 1], DataType.int64, "bias")
+    bias.values = np.array([0x01FF_FFFF_FFFF])
+    op.add_input_tensor(bias)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_batch_size():
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [2, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1])
+    op.attrs = {"stride_w": 1, "stride_h": 1}
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_depth_multiplier():
+    # Valid. Depth multiplier is 1 so no further constraints
+    op = testutil.create_op_with_quant_tensors(
+        Op.DepthwiseConv2DBias, [1, 1, 1, 1], [1, 1, 1, 2], weights_shape=[1, 1, 1, 1]
+    )
+    op.attrs = {"stride_w": 1, "stride_h": 1, "depth_multiplier": 1}
+    assert support.is_operator_supported(op)
+    # Invalid. Depth multiplier doesnt equal ofm channel
+    op = testutil.create_op_with_quant_tensors(
+        Op.DepthwiseConv2DBias, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1]
+    )
+    op.attrs = {"stride_w": 1, "stride_h": 1, "depth_multiplier": 2}
+    assert not support.is_operator_supported(op)
+    # Valid. Depth multiplier is equal to ofm channel
+    op = testutil.create_op_with_quant_tensors(
+        Op.DepthwiseConv2DBias, [1, 1, 1, 1], [1, 1, 1, 2], weights_shape=[1, 1, 1, 1]
+    )
+    op.attrs = {"stride_w": 1, "stride_h": 1, "depth_multiplier": 2}
+    assert support.is_operator_supported(op)
+
+
+def test_constraint_tconv_stride():
+    # Strides must be 2
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 2, 2, 1], weights_shape=[1, 1, 1, 1])
+    op.attrs = {"stride_w": 1, "stride_h": 1, "padding": Padding.SAME}
+    ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
+    ifm.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_tconv_same():
+    # Valid
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 2, 2, 1], weights_shape=[1, 1, 1, 1])
+    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.SAME}
+    ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
+    ifm.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm)
+    assert support.is_operator_supported(op)
+    # Invalid
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[1, 1, 1, 1])
+    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.SAME}
+    ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
+    ifm.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_tconv_valid():
+    # Valid
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[4, 4, 1, 1])
+    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.VALID}
+    ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
+    ifm.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm)
+    assert support.is_operator_supported(op)
+    # Invalid
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[2, 2, 1, 1])
+    op.attrs = {"stride_w": 2, "stride_h": 2, "padding": Padding.VALID}
+    ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
+    ifm.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_filter_range():
+    # Avg pool restrictions are dependent on padding:
+    # SAME padding restricts both W and H to max 8
+    op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 20, "filter_height": 20, "padding": Padding.SAME}
+    assert not support.is_operator_supported(op)
+    # VALID padding limits are much larger
+    op.attrs["padding"] = Padding.VALID
+    assert support.is_operator_supported(op)
+
+
+def test_constraint_filter_height_range_valid_pad():
+    # Avg pool restrictions are dependent on padding:
+    op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 256, "padding": Padding.VALID}
+    assert support.is_operator_supported(op)
+    # VALID padding restricts to 256 in filter height
+    op.attrs["filter_height"] = 257
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_filter_product_height_range_valid_pad():
+    # Avg pool restrictions are dependent on padding:
+    op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 256, "filter_height": 256, "padding": Padding.VALID}
+    assert support.is_operator_supported(op)
+    # VALID padding restricts filter W x H to 256x256
+    op.attrs["filter_width"] = 257
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_filter_height_range():
+    # Max pool restrictions arent dependent on padding
+    op = testutil.create_op_with_quant_tensors(Op.MaxPool, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 256, "padding": Padding.SAME}
+    assert support.is_operator_supported(op)
+    # Restricts to 256 in filter height
+    op.attrs["filter_height"] = 257
+    assert not support.is_operator_supported(op)
+    # Doesnt matter if SAME or VALID
+    op.attrs["padding"] = Padding.VALID
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_filter_product_height_range():
+    # Max pool restrictions arent dependent on padding
+    op = testutil.create_op_with_quant_tensors(Op.MaxPool, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 256, "filter_height": 256, "padding": Padding.SAME}
+    assert support.is_operator_supported(op)
+    # Restricts filter W x H to 256x256
+    op.attrs["filter_width"] = 257
+    assert not support.is_operator_supported(op)
+    # Doesnt matter if SAME or VALID
+    op.attrs["padding"] = Padding.VALID
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_resize():
+    # IFM W and H == 1
+    op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 1, 1, 8], [1, 8, 8, 8])
+    assert support.is_operator_supported(op)
+    # IFM == OFM
+    op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 8, 8, 8], [1, 8, 8, 8])
+    assert support.is_operator_supported(op)
+    # IFM x2 == OFM ; align_corners = False
+    op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 8, 8, 8])
+    assert support.is_operator_supported(op)
+    # IFM x2 -1 == OFM ; align_corners = True
+    op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 7, 7, 8])
+    op.attrs["align_corners"] = True
+    assert support.is_operator_supported(op)
+    # Invalid cases
+    op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 20, 20, 8])
+    assert not support.is_operator_supported(op)
+    op.attrs["align_corners"] = True
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_concat_pass():
+    # A working concat
+    op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
+    ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2")
+    ifm2.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm2)
+    op.attrs["axis"] = 3
+    assert support.is_operator_supported(op)
+
+
+def create_pad_op(
+    in_shape, out_shape, padding, in_dtype=DataType.int8, out_dtype=DataType.int8, pad_dtype=DataType.int32,
+):
+    qp = testutil.default_quant_params()
+    in0 = Tensor(in_shape, in_dtype, "in")
+    in0.quantization = qp
+    pad_tensor = create_const_tensor(name="pad", shape=list(np.shape(padding)), values=padding, dtype=pad_dtype)
+    out = Tensor(out_shape, out_dtype, "out")
+    out.quantization = qp.clone()
+    op = testutil.create_op(Op.Pad, [in0, pad_tensor], out)
+    return op
+
+
+def test_constraint_padded_dimensions():
+    # Incorrect padding dimensions, can only pad width and height
+    op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[1, 1], [1, 1], [1, 1], [0, 0]],)
+    assert not support.is_operator_supported(op)
+    op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[1, 1], [1, 1], [0, 0]],)
+    assert support.is_operator_supported(op)
+    op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[1, 1], [1, 1], [0, 1]],)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_pad_shape():
+    # PAD operator must be of shape (3,2) or (4,2)
+    op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[1, 1], [1, 1], [0, 0]])
+    assert support.is_operator_supported(op)
+    op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[0, 0], [1, 1], [1, 1], [0, 0], [0, 0]],)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_pad_none():
+    op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[],)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_pad_dtype():
+    # PAD operator dtype should be int32 or int64
+    op = create_pad_op(
+        in_shape=[1, 1, 1, 1],
+        out_shape=[1, 3, 3, 1],
+        padding=[[0, 0], [1, 1], [1, 1], [0, 0], [0, 0]],
+        pad_dtype=DataType.int16,
+    )
+    assert not support.is_operator_supported(op)
+
+
+def create_strided_slice_op(in_shape, out_shape, start_offsets, end_offsets):
+    qp = testutil.default_quant_params()
+    in0 = Tensor(in_shape, DataType.uint8, "in")
+    in0.quantization = qp
+    in1 = create_const_tensor("begin", [len(start_offsets)], DataType.uint8, start_offsets, quantization=qp)
+    in2 = create_const_tensor("end", [len(end_offsets)], DataType.uint8, end_offsets, quantization=qp)
+    in3 = create_const_tensor("strides", [len(end_offsets)], DataType.uint8, len(end_offsets) * [1], quantization=qp)
+    out = Tensor(out_shape, DataType.uint8, "out")
+    out.quantization = qp
+    attrs = {"ellipsis_mask": 0, "new_axis_mask": 0, "shrink_axis_mask": 0, "begin_mask": 0, "end_mask": 0}
+    return testutil.create_op(Op.StridedSlice, [in0, in1, in2, in3], out, attrs=attrs)
+
+
+def create_strided_slice():
+    # Creates a valid strided slice operator with some valid inputs/outputs
+    op = create_strided_slice_op([1, 10, 10, 10], [1, 5, 5, 10], [127, 2, 2, 0], [0, 7, -3, 0])
+    op.attrs["begin_mask"] = 1
+    op.attrs["end_mask"] = 9
+    assert support.is_operator_supported(op)
+    return op
+
+
+def test_constraint_stridedslice_stride_values():
+    # Unsupported strides
+    op = create_strided_slice()
+    op.inputs[3].values = [1, 1, 2, 1]
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_inputs_int32():
+    # both inputs must be type int32
+    op = testutil.create_elemwise_op(Op.SHL, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
+    assert not support.is_operator_supported(op)
+    op = testutil.create_elemwise_op(Op.SHL, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32)
+    assert support.is_operator_supported(op)
+    op.ifm2.dtype = DataType.int16
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_output_int32():
+    # output must be type int32
+    op = testutil.create_elemwise_op(Op.SHL, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32)
+    assert support.is_operator_supported(op)
+    op.ofm.dtype = DataType.int16
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_matching_quantization_parameters():
+    qp = QuantizationParameters()
+    qp.scale_f32 = np.float32(1.5)
+    qp.zero_point = 128
+    # valid - all matching (uses default quant params)
+    op = testutil.create_elemwise_op(Op.Minimum, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
+    assert support.is_operator_supported(op)
+    # invalid - ifm mismatch ofm
+    op.ifm.quantization = qp
+    assert not support.is_operator_supported(op)
+    # invalid - ifm2 mismatch ofm
+    op = testutil.create_elemwise_op(Op.Minimum, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
+    op.ifm2.quantization = qp
+    assert not support.is_operator_supported(op)
+    # invalid - both ifm and ifm2 mismatch ofm
+    op = testutil.create_elemwise_op(Op.Minimum, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
+    op.ifm.quantization = qp
+    op.ifm2.quantization = qp
+    assert not support.is_operator_supported(op)
+    # valid - all matching
+    op.ofm.quantization = qp
+    assert support.is_operator_supported(op)
+    op = testutil.create_elemwise_op(Op.Minimum, "op", [1, 8, 8, 8], None, [1, 8, 8, 8])
+    assert support.is_operator_supported(op)
+
+
+def test_constraint_elemwise_batch_size():
+    # BINARY CASE
+    # Batch can be >1 if dims is <=2D
+    op = testutil.create_elemwise_op(Op.Add, "op", [2, 2], [2, 2], [2, 2])
+    assert support.is_operator_supported(op)
+    # For dims >2D, batch must be 1
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 2, 2], [1, 2, 2], [1, 2, 2])
+    assert support.is_operator_supported(op)
+    # invalid case
+    op = testutil.create_elemwise_op(Op.Add, "op", [2, 2, 2], [2, 2, 2], [2, 2, 2])
+    assert not support.is_operator_supported(op)
+
+    # UNARY CASE
+    # Batch can be >1 if dims is <=2D
+    op = testutil.create_elemwise_op(Op.CLZ, "op", [2, 2], None, [2, 2], datatype=DataType.int32)
+    assert support.is_operator_supported(op)
+    # For dims >2D, batch must be 1
+    op = testutil.create_elemwise_op(Op.CLZ, "op", [1, 2, 2], None, [1, 2, 2], datatype=DataType.int32)
+    assert support.is_operator_supported(op)
+    # invalid case
+    op = testutil.create_elemwise_op(Op.CLZ, "op", [2, 2, 2], None, [2, 2, 2], datatype=DataType.int32)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_broadcast_shapes():
+    # BINARY CASE
+    # Only allow broadcast to 1 dim, for 1 rank index
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 1, 4], [1, 2, 4], [1, 2, 4])
+    assert support.is_operator_supported(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 2, 4], [1, 1, 4], [1, 2, 4])
+    assert support.is_operator_supported(op)
+    # Only allow broadcast to 1 dim, for 3 rank indexes
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 1, 1, 1], [1, 4, 8, 16], [1, 4, 8, 16])
+    assert support.is_operator_supported(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 4, 8, 16], [1, 1, 1, 1], [1, 4, 8, 16])
+    assert support.is_operator_supported(op)
+    # One broadcast dim not 1
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 2, 4], [1, 4, 4], [1, 4, 4])
+    assert not support.is_operator_supported(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 4, 4], [1, 2, 4], [1, 4, 4])
+    assert not support.is_operator_supported(op)
+    # OFM shape dim largest ifm/ifm2 shape dim
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 4], [4, 4], [1, 4])
+    assert not support.is_operator_supported(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 4], [4, 4], [1, 4])
+    assert not support.is_operator_supported(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 4, 1, 16], [1, 1, 4, 1], [1, 4, 1, 16])
+    assert not support.is_operator_supported(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 1, 4, 1], [1, 4, 1, 16], [1, 4, 1, 16])
+    assert not support.is_operator_supported(op)
+
+
+def create_mean(input_shape, output_shape, axis, datatype, attrs):
+    ifm = Tensor(input_shape, datatype, "in")
+    ifm.quantization = testutil.default_quant_params()
+    ofm = Tensor(output_shape, datatype, "out")
+    ofm.quantization = testutil.default_quant_params()
+    if type(axis) is list:
+        indices = create_const_tensor("indices", [len(axis)], DataType.int32, axis, np.uint8)
+    elif type(axis) is int:
+        indices = create_const_tensor("indices", [], DataType.int32, axis, np.uint8)
+    op = testutil.create_op(Op.Mean, [ifm, indices], ofm, attrs)
+    return op
+
+
+def test_mean_hw_product():
+    op = create_mean([1, 64, 64, 16], [1, 16], [1, 2], DataType.uint8, {})
+    assert support.is_operator_supported(op)
+    op = create_mean([1, 65, 64, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True})
+    assert not support.is_operator_supported(op)
+
+
+def test_mean_hw_product_int8():
+    op = create_mean([1, 16, 16, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True})
+    assert support.is_operator_supported(op)
+    op = create_mean([1, 16, 17, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True})
+    assert not support.is_operator_supported(op)
+
+
+def test_mean_hw_product_avgpool():
+    op = create_mean([1, 200, 200, 16], [1, 16], [1, 2], DataType.uint8, {"keep_dims": False})
+    assert support.is_operator_supported(op)
+    op = create_mean([1, 200, 200, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True})
+    assert not support.is_operator_supported(op)