diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py
index 665ebc2..595ea59 100644
--- a/ethosu/vela/test/test_supported_operators.py
+++ b/ethosu/vela/test/test_supported_operators.py
@@ -29,67 +29,9 @@
 support = SupportedOperators()
 
 
-def create_strided_slice_op(in_shape, out_shape, start_offsets, end_offsets):
-    qp = QuantizationParameters()
-    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_strided_slice():
-    # Tests support for StridedSlice operator
-    op = create_strided_slice()
-    # Setting one of new_axis_mask/shrink_axis_mask to non-zero is ok
-    op.attrs["new_axis_mask"] = 2
-    assert support.is_operator_supported(op)
-    op = create_strided_slice()
-    op.attrs["shrink_axis_mask"] = 3
-    assert support.is_operator_supported(op)
-    # But setting both to non-zero is not supported
-    op.attrs["new_axis_mask"] = 2
-    assert not support.is_operator_supported(op)
-    # begin values must not be None
-    op.inputs[1].values = None
-    assert not support.is_operator_supported(op)
-    # Unsupported strides
-    op = create_strided_slice()
-    op.inputs[3].values = [1, 1, 2, 1]
-    assert not support.is_operator_supported(op)
-    # Wrong number of input tensors
-    op = create_strided_slice()
-    op.add_input_tensor(op.inputs[0].clone())
-    assert not support.is_operator_supported(op)
-    # Unsupported ellipsis mask
-    op = create_strided_slice()
-    op.attrs["ellipsis_mask"] = 1
-    assert not support.is_operator_supported(op)
-    # Examples where end offset <= begin offset
-    op = create_strided_slice()
-    op.inputs[1].values = [0, 7, 2, 0]
-    assert not support.is_operator_supported(op)
-    op = create_strided_slice()
-    op.inputs[2].values = [0, 7, 2, 0]
-    assert not support.is_operator_supported(op)
-    op = create_strided_slice()
-    op.attrs["begin_mask"] = 0
-    assert not support.is_operator_supported(op)
-    op = create_strided_slice()
-    op.attrs["end_mask"] = 0
+def test_constraint_tens_no_dynamic():
+    # Tensors cannot be dynamic (no shape, not a scalar)
+    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [])
     assert not support.is_operator_supported(op)
 
 
@@ -99,18 +41,20 @@
     assert not support.is_operator_supported(op)
 
 
-def test_constraint_tens_output_shapeless():
-    # Shapeless output is not allowed at all:
+def test_constraint_tens_output_scalar():
+    # Scalar output is not allowed at all:
     op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [])
+    op.ofm.values = 0.5
     assert not support.is_operator_supported(op)
 
 
-def test_constraint_tens_input_shapeless():
+def test_constraint_tens_input_scalar():
     # Shapeless input is allowed if its of a certain type:
     op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8])
     assert support.is_operator_supported(op)
     # Invalid shapeless input due to op type:
     op = testutil.create_op_with_quant_tensors(Op.Relu, [], [1, 8, 8, 8])
+    op.ifm.values = 0.5
     assert not support.is_operator_supported(op)
 
 
@@ -149,6 +93,7 @@
 def test_constraint_tens_quant_scale():
     # Quantization scale cannot be infinit
     qp = QuantizationParameters()
+    qp.zero_point = 0
     qp.scale_f32 = np.inf
     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)
@@ -219,12 +164,12 @@
     assert not support.is_operator_supported(op)
 
 
-def test_constraint_weights_nonconst():
+def test_constraint_weights_const():
     # Weight tensor cannot be non-const tensors
     op = testutil.create_op_with_quant_tensors(Op.Conv2D, [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 = QuantizationParameters()
+    weights.quantization = testutil.default_quant_params()
     op.add_input_tensor(weights)
     assert not support.is_operator_supported(op)
 
@@ -251,7 +196,7 @@
     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.quant_values = np.array([0x1FF_FFFF_FFFF])
+    bias.quant_values = np.array([0x01FF_FFFF_FFFF])
     op.add_input_tensor(bias)
     assert not support.is_operator_supported(op)
 
@@ -260,3 +205,452 @@
     op = testutil.create_op_with_quant_tensors(Op.Conv2D, [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_quant_scale_inf():
+    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.ofm.quantization.scale_f32 = np.float32(1e-39)
+    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": b"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": b"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": b"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": b"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": b"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_matching_in_out_types():
+    # Valid
+    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": 2, "padding": b"SAME"}
+    assert support.is_operator_supported(op)
+    # Invalid. datatypes for ifm and ofm must match (default uint8)
+    op.ifm.dtype = DataType.int8
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_filter_type():
+    # Filter width/height must be integers
+    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": 2.5, "filter_height": "2", "padding": b"SAME"}
+    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": b"SAME"}
+    assert not support.is_operator_supported(op)
+    # VALID padding limits are much larger
+    op.attrs["padding"] = b"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": b"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": b"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": b"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"] = b"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": b"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"] = b"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_matching_shapes():
+    # Softmax requires the ifm and ofm shapes to match
+    op = testutil.create_op_with_quant_tensors(Op.Softmax, [1, 1, 1, 8], [1, 2, 2, 4])
+    assert not support.is_operator_supported(op)
+    op = testutil.create_op_with_quant_tensors(Op.Softmax, [1, 1, 1, 8], [1, 1, 1, 8])
+    assert support.is_operator_supported(op)
+
+
+def test_constraint_splitv_inferred():
+    # SplitV requires a maximum of one inferred shape (-1)
+    qp = testutil.default_quant_params()
+    op = testutil.create_op_with_quant_tensors(Op.SplitV, [1, 1, 1, 8], [1, 1, 1, 8])
+    sizes = create_const_tensor("sizes", [1, 1, 1, 4], DataType.int16, [[[[0, -1, 2, -1]]]], np.int16, quantization=qp)
+    op.add_input_tensor(sizes)
+    assert not support.is_operator_supported(op)
+    op = testutil.create_op_with_quant_tensors(Op.SplitV, [1, 1, 1, 8], [1, 1, 1, 8])
+    sizes = create_const_tensor("sizes", [1, 1, 1, 4], DataType.int16, [[[[0, 1, 2, -1]]]], np.int16, quantization=qp)
+    op.add_input_tensor(sizes)
+    assert 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 test_constraint_axis_exists():
+    # Missing axis attribute
+    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)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_axis_valid():
+    # Invalid axis attribute
+    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"] = 7
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_matching_dimensionality():
+    # Mismatching dimensionality: 4D+2D=4D
+    op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
+    ifm2 = Tensor([1, 4], DataType.uint8, "in2")
+    ifm2.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm2)
+    op.attrs["axis"] = 3
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_valid_dimensions():
+    # Mismatching dimension value:
+    # ifm2 has w and h as 2, which is not the axis to concat and doesnt match ifm1 or ofm
+    op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
+    ifm2 = Tensor([1, 2, 2, 4], DataType.uint8, "in2")
+    ifm2.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm2)
+    op.attrs["axis"] = 3
+    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_input_count():
+    # Wrong number of input tensors
+    op = create_strided_slice()
+    op.add_input_tensor(op.inputs[0].clone())
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_stridedslice_inputs_const():
+    # begin, end, stride values must not be None
+    op = create_strided_slice()
+    op.inputs[1].values = None
+    assert not support.is_operator_supported(op)
+    op = create_strided_slice()
+    op.inputs[2].values = None
+    assert not support.is_operator_supported(op)
+    op = create_strided_slice()
+    op.inputs[3].values = None
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_stridedslice_tens_size_matches():
+    op = create_strided_slice()
+    op.inputs[1].values = [1, 1, 1, 1, 1, 1, 1, 1]
+    assert not support.is_operator_supported(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_ellipsis_mask():
+    # Unsupported ellipsis mask
+    op = create_strided_slice()
+    op.attrs["ellipsis_mask"] = 1
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_axis_masks():
+    op = create_strided_slice()
+    # Setting one of new_axis_mask/shrink_axis_mask to non-zero is ok
+    op.attrs["new_axis_mask"] = 2
+    assert support.is_operator_supported(op)
+    op = create_strided_slice()
+    op.attrs["shrink_axis_mask"] = 3
+    assert support.is_operator_supported(op)
+    # But setting both to non-zero is not supported
+    op.attrs["new_axis_mask"] = 2
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_slice_ranges():
+    # Examples where end offset <= begin offset
+    op = create_strided_slice()
+    op.inputs[1].values = [0, 7, 2, 0]
+    assert not support.is_operator_supported(op)
+    op = create_strided_slice()
+    op.inputs[2].values = [0, 7, 2, 0]
+    assert not support.is_operator_supported(op)
+    op = create_strided_slice()
+    op.attrs["begin_mask"] = 0
+    assert not support.is_operator_supported(op)
+    op = create_strided_slice()
+    op.attrs["end_mask"] = 0
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_matching_inputs_types():
+    # input data types must match (default is uint8)
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
+    op.ifm2.dtype = DataType.int8
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_matching_signed():
+    # signed inputs require output to also be signed
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int8)
+    op.ofm.dtype = DataType.uint8
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_unsigned_valid():
+    # unsigned inputs require output to be either:
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
+    # the same (default uint8)
+    assert support.is_operator_supported(op)
+    op.ofm.dtype = DataType.int8
+    assert not support.is_operator_supported(op)
+    op.ofm.dtype = DataType.int16
+    assert not support.is_operator_supported(op)
+    # or int32
+    op.ofm.dtype = DataType.int32
+    assert 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)
+
+
+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_matching_either_shapes():
+    # BINARY CASE
+    # At least one ifm shape must match ofm's shape
+    op = testutil.create_elemwise_op(Op.Add, "op", [2, 2], [4, 4], [2, 2])
+    assert support.is_operator_supported(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [4, 4], [2, 2], [2, 2])
+    assert support.is_operator_supported(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [4, 4], [4, 4], [2, 2])
+    assert not support.is_operator_supported(op)
+
+    # UNARY CASE
+    # No second input so this is treated the same as requiring ifm shape to match ofm shape
+    op = testutil.create_elemwise_op(Op.CLZ, "op", [2, 2], None, [2, 2], datatype=DataType.int32)
+    assert support.is_operator_supported(op)
+    op = testutil.create_elemwise_op(Op.CLZ, "op", [4, 4], None, [2, 2], datatype=DataType.int32)
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_alpha_valid():
+    # Alpha cannot be negative
+    op = testutil.create_elemwise_op(Op.LeakyRelu, "op", [2, 2], None, [2, 2])
+    op.attrs["alpha"] = 0
+    assert support.is_operator_supported(op)
+    op.attrs["alpha"] = -1
+    assert not support.is_operator_supported(op)