diff --git a/ethosu/vela/graph_optimiser_util.py b/ethosu/vela/graph_optimiser_util.py
index 570c724..d01d4a1 100644
--- a/ethosu/vela/graph_optimiser_util.py
+++ b/ethosu/vela/graph_optimiser_util.py
@@ -17,14 +17,17 @@
 # Common functions and definitions used during the graph optimization.
 from typing import Tuple
 
+import numpy as np
+
 from .data_type import DataType
 from .debug_database import DebugDatabase
+from .errors import UnsupportedFeatureError
 from .errors import VelaError
 from .operation import Op
+from .operation_util import create_avgpool_nop
 from .shape4d import Shape4D
 from .tensor import check_quantized_tens_scaling_equal
 
-
 memory_only_ops = (
     Op.Reshape,
     Op.Squeeze,
@@ -174,6 +177,41 @@
     return op
 
 
+def bypass_reshape_and_squeeze_ops(op):
+    assert op.type in (Op.Reshape, Op.Squeeze)
+    ofm = op.ofm
+    ifm = op.ifm
+    # Check if ifm/ofm are network ifm/ofm
+    ifm_is_sg_ifm = ifm.ops[0].type in (Op.Placeholder, Op.SubgraphInput, Op.Const)
+    ifm_is_sg_ofm = any(ifm_cons is None for ifm_cons in ifm.consumer_list)
+    ofm_is_sg_ofm = any(ofm_cons is None for ofm_cons in ofm.consumer_list)
+    # Check if ifm/ofm is produced respectively consumed by CPU
+    ifm_is_cpu_produced = any(ifm_prod is not None and not ifm_prod.run_on_npu for ifm_prod in op.ifm.ops)
+    ofm_is_cpu_consumed = any(ofm_cons is not None and not ofm_cons.run_on_npu for ofm_cons in op.ofm.consumer_list)
+
+    # This case should be handled prior to this function
+    assert not ((ifm_is_sg_ifm or ifm_is_sg_ofm or ifm_is_cpu_produced) and (ofm_is_sg_ofm or ofm_is_cpu_consumed))
+
+    if ofm_is_sg_ofm or ofm_is_cpu_consumed:
+        # Bypassed by replacing ifm with ofm
+        ofm.ops = []
+        for prev_op in ifm.ops:
+            prev_op.outputs = [ofm]
+            ofm.ops.append(prev_op)
+
+        # All ifm consumers need to use ofm as input
+        for ifm_cons in ifm.consumer_list:
+            for ifm_idx, cons_ifm in enumerate(ifm_cons.inputs):
+                if cons_ifm == ifm:
+                    ifm_cons.set_input_tensor(ofm, ifm_idx)
+    else:
+        # Bypassed by replacing ofm with ifm
+        for cons in ofm.consumer_list:
+            for ifm_idx, cons_ifm in enumerate(cons.inputs):
+                if cons_ifm == ofm:
+                    cons.set_input_tensor(ifm, ifm_idx)
+
+
 def check_reshapes(op, arch):
     if op.run_on_npu and op.type == Op.Reshape:
         ofm = op.ofm
@@ -186,3 +224,77 @@
 def record_optimised(op, arch):
     if op.type != Op.Const:
         DebugDatabase.add_optimised(op, op)
+
+
+def insert_copy_op_after_tens(tens):
+    tens_cons_list_copy = tens.consumer_list.copy()
+
+    # Create a avg_pool nop op with ifm as input
+    copy_tens = tens.clone()
+    copy_op = create_avgpool_nop(tens.name + "_avgpool")
+    copy_op.add_input_tensor(tens)
+    copy_op.set_output_tensor(copy_tens)
+    copy_op.set_ifm_ofm_shapes()
+    copy_op.run_on_npu = True
+
+    # Set copy_ifm consumers
+    for tens_cons in tens_cons_list_copy:
+        if tens_cons is not None:
+            for ifm_idx, cons_inp in enumerate(tens_cons.inputs):
+                if cons_inp == tens:
+                    tens_cons.set_input_tensor(copy_tens, ifm_idx)
+
+    DebugDatabase.add_optimised(tens.ops[0], copy_op)
+
+
+def fix_sg_input_output(op, arch, nng):
+    if not op.run_on_npu or op.type not in (Op.Reshape, Op.Squeeze):
+        return op
+
+    # For the Reshape/Squeeze operators we want to remove, tensors are removed.
+    # But in order to to do this, they cannot be outputs of the sg,
+    # this need to be fixed prior to the removal.
+    # Solution is to add a avgpool NOP, to maintain the original tensor.
+    # This is also valid when reshape ifm/ofm is produced respectively
+    # consumed by CPU
+
+    # Check if operator ifm/ofm are sg ifm/ofm
+    ifm_is_sg_ifm = op.ifm.ops[0].type in (Op.Placeholder, Op.SubgraphInput, Op.Const)
+    ifm_is_sg_ofm = any(ifm_cons is None for ifm_cons in op.ifm.consumer_list)
+    ofm_is_sg_ofm = any(ofm_cons is None for ofm_cons in op.ofm.consumer_list)
+    # Check if ifm/ofm is produced respectively consumed by CPU
+    ifm_is_cpu_produced = any(ifm_prod is not None and not ifm_prod.run_on_npu for ifm_prod in op.ifm.ops)
+    ofm_is_cpu_consumed = any(ofm_cons is not None and not ofm_cons.run_on_npu for ofm_cons in op.ofm.consumer_list)
+
+    if (ifm_is_sg_ofm or ifm_is_sg_ifm or ifm_is_cpu_produced) and (ofm_is_sg_ofm or ofm_is_cpu_consumed):
+        # Both ifm and ofm need to persist, but only ifm need a copy, in order to remove the Reshape/Squeeze
+        insert_copy_op_after_tens(op.ifm)
+
+    return op
+
+
+def convert_depthwise_to_conv(op, arch, nng):
+    # Depthwise is equivalent to a single conv2d if the ifm depth is 1 and
+    # the ofm depth equals the depth multipler.
+    # If those conditions are true, then we can perform a simple
+    # switch of the operator type (and weight order)
+
+    if op.type == Op.DepthwiseConv2DBias and (op.attrs["depth_multiplier"] != 1):
+        ifm_shape = op.ifm_shapes[0]
+        weight_tensor = op.inputs[1]
+        ofm_shape = op.ofm_shapes[0]
+        if (ifm_shape.depth == 1) and (ofm_shape.depth == op.attrs["depth_multiplier"]):
+            # Change op type to Conv2d
+            op.type = Op.Conv2DBias
+            del op.attrs["channel_multiplier"]
+            del op.attrs["depth_multiplier"]
+
+            weight_tensor.values = np.transpose(weight_tensor.values, (0, 1, 3, 2))
+            weight_tensor.set_all_shapes(list(weight_tensor.values.shape))
+        else:
+            raise UnsupportedFeatureError(
+                f"Unsupported 'DEPTHWISE_CONV_2D' with depth_multiplier = {op.attrs['depth_multiplier']},",
+                f" ifm channels = {ifm_shape.depth}, ofm channels = {ofm_shape.depth}",
+            )
+        DebugDatabase.add_optimised(op, op)
+    return op
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index ca833e2..80be228 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -283,7 +283,7 @@
     Tanh = OperatorInfo(indices=NNG_IFM_INDICES)
     Tile = OperatorInfo()
     TopKV2 = OperatorInfo()
-    Transpose = OperatorInfo()
+    Transpose = OperatorInfo(indices=NNG_IFM_INDICES)
     UnidirectionalSequenceLstm = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_IFM_WEIGHTS_INDICES)
     UnidirectionalSequenceRnn = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_IFM_WEIGHTS_INDICES)
     Unique = OperatorInfo()
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py
index 255a1f5..ef39aea 100644
--- a/ethosu/vela/tflite_graph_optimiser.py
+++ b/ethosu/vela/tflite_graph_optimiser.py
@@ -30,7 +30,10 @@
 from .debug_database import DebugDatabase
 from .errors import UnsupportedFeatureError
 from .ethos_u55_regs.ethos_u55_regs import resampling_mode
+from .graph_optimiser_util import bypass_reshape_and_squeeze_ops
 from .graph_optimiser_util import calc_explicit_padding
+from .graph_optimiser_util import convert_depthwise_to_conv
+from .graph_optimiser_util import fix_sg_input_output
 from .graph_optimiser_util import needed_total_padding
 from .graph_optimiser_util import set_ifm_ofm_op_shapes
 from .graph_optimiser_util import set_tensor_equivalence
@@ -244,32 +247,6 @@
     DebugDatabase.add_optimised(tens.ops[0], copy_op)
 
 
-def fix_sg_input_output(op, arch, nng):
-    if not op.run_on_npu or op.type not in (Op.Reshape, Op.Squeeze):
-        return op
-
-    # For the Reshape/Squeeze operators we want to remove, tensors are removed.
-    # But in order to to do this, they cannot be outputs of the sg,
-    # this need to be fixed prior to the removal.
-    # Solution is to add a avgpool NOP, to maintain the original tensor.
-    # This is also valid when reshape ifm/ofm is produced respectively
-    # consumed by CPU
-
-    # Check if operator ifm/ofm are sg ifm/ofm
-    ifm_is_sg_ifm = op.ifm.ops[0].type in (Op.Placeholder, Op.SubgraphInput, Op.Const)
-    ifm_is_sg_ofm = any(ifm_cons is None for ifm_cons in op.ifm.consumer_list)
-    ofm_is_sg_ofm = any(ofm_cons is None for ofm_cons in op.ofm.consumer_list)
-    # Check if ifm/ofm is produced respectively consumed by CPU
-    ifm_is_cpu_produced = any(ifm_prod is not None and not ifm_prod.run_on_npu for ifm_prod in op.ifm.ops)
-    ofm_is_cpu_consumed = any(ofm_cons is not None and not ofm_cons.run_on_npu for ofm_cons in op.ofm.consumer_list)
-
-    if (ifm_is_sg_ofm or ifm_is_sg_ifm or ifm_is_cpu_produced) and (ofm_is_sg_ofm or ofm_is_cpu_consumed):
-        # Both ifm and ofm need to persist, but only ifm need a copy, in order to remove the Reshape/Squeeze
-        insert_copy_op_after_tens(op.ifm)
-
-    return op
-
-
 def calc_padding_and_skirt(padding_type, kernel, input_shape, explicit_padding):
     k_w, k_h = kernel.dilated_wh()
     s_x, s_y = kernel.stride
@@ -576,33 +553,6 @@
     return op
 
 
-def convert_depthwise_to_conv(op, arch, nng):
-    # Depthwise is equivalent to a single conv2d if the ifm depth is 1 and
-    # the ofm depth equals the depth multipler.
-    # If those conditions are true, then we can perform a simple
-    # switch of the operator type (and weight order)
-
-    if op.type == Op.DepthwiseConv2DBias and (op.attrs["depth_multiplier"] != 1):
-        ifm_shape = op.ifm_shapes[0]
-        weight_tensor = op.inputs[1]
-        ofm_shape = op.ofm_shapes[0]
-        if (ifm_shape.depth == 1) and (ofm_shape.depth == op.attrs["depth_multiplier"]):
-            # Change op type to Conv2d
-            op.type = Op.Conv2DBias
-            del op.attrs["channel_multiplier"]
-            del op.attrs["depth_multiplier"]
-
-            weight_tensor.values = np.transpose(weight_tensor.values, (0, 1, 3, 2))
-            weight_tensor.set_all_shapes(list(weight_tensor.values.shape))
-        else:
-            raise UnsupportedFeatureError(
-                f"Unsupported 'DEPTHWISE_CONV_2D' with depth_multiplier = {op.attrs['depth_multiplier']},",
-                f" ifm channels = {ifm_shape.depth}, ofm channels = {ofm_shape.depth}",
-            )
-        DebugDatabase.add_optimised(op, op)
-    return op
-
-
 def reorder_depthwise_weights(op, arch, nng):
     if op.type.is_depthwise_conv2d_op():
         weight_tensor = op.inputs[1]
@@ -1058,35 +1008,7 @@
             # or the reshape need to be replace with a NOP.
             return
 
-        # Check if ifm/ofm are network ifm/ofm
-        ifm_is_sg_ifm = ifm.ops[0].type in (Op.Placeholder, Op.SubgraphInput, Op.Const)
-        ifm_is_sg_ofm = any(ifm_cons is None for ifm_cons in ifm.consumer_list)
-        ofm_is_sg_ofm = any(ofm_cons is None for ofm_cons in ofm.consumer_list)
-        # Check if ifm/ofm is produced respectively consumed by CPU
-        ifm_is_cpu_produced = any(ifm_prod is not None and not ifm_prod.run_on_npu for ifm_prod in op.ifm.ops)
-        ofm_is_cpu_consumed = any(ofm_cons is not None and not ofm_cons.run_on_npu for ofm_cons in op.ofm.consumer_list)
-
-        # This case should be handled prior to this function
-        assert not ((ifm_is_sg_ifm or ifm_is_sg_ofm or ifm_is_cpu_produced) and (ofm_is_sg_ofm or ofm_is_cpu_consumed))
-
-        if ofm_is_sg_ofm or ofm_is_cpu_consumed:
-            # Bypassed by replacing ifm with ofm
-            ofm.ops = []
-            for prev_op in ifm.ops:
-                prev_op.outputs = [ofm]
-                ofm.ops.append(prev_op)
-
-            # All ifm consumers need to use ofm as input
-            for ifm_cons in ifm.consumer_list:
-                for ifm_idx, cons_ifm in enumerate(ifm_cons.inputs):
-                    if cons_ifm == ifm:
-                        ifm_cons.set_input_tensor(ofm, ifm_idx)
-        else:
-            # Bypassed by replacing ofm with ifm
-            for cons in ofm.consumer_list:
-                for ifm_idx, cons_ifm in enumerate(cons.inputs):
-                    if cons_ifm == ofm:
-                        cons.set_input_tensor(ifm, ifm_idx)
+        bypass_reshape_and_squeeze_ops(op)
 
 
 def fuse_activation_function_with_prev(op, arch, nng):
diff --git a/ethosu/vela/tosa_graph_optimiser.py b/ethosu/vela/tosa_graph_optimiser.py
index 44e0f8e..169da40 100644
--- a/ethosu/vela/tosa_graph_optimiser.py
+++ b/ethosu/vela/tosa_graph_optimiser.py
@@ -19,7 +19,10 @@
 from .api import NpuRoundingMode
 from .data_type import DataType
 from .debug_database import DebugDatabase
+from .graph_optimiser_util import bypass_reshape_and_squeeze_ops
 from .graph_optimiser_util import calc_explicit_padding
+from .graph_optimiser_util import convert_depthwise_to_conv
+from .graph_optimiser_util import fix_sg_input_output
 from .graph_optimiser_util import needed_total_padding
 from .graph_optimiser_util import set_ifm_ofm_op_shapes
 from .graph_optimiser_util import set_tensor_equivalence
@@ -74,6 +77,43 @@
     return op
 
 
+def remove_const_transpose(op, arch, nng):
+    if op.type == Op.Transpose:
+        removed = False
+        if len(op.ifm.ops) == 1:
+            prev_op = op.ifm.ops[0]
+            if prev_op.type == Op.Const:
+                # Transpose the Tensor and data and remove Transpose
+                # TODO move to Tensor?
+                reorder = op.attrs["perms"]
+                shape = op.ifm.shape.copy()
+                tens = op.ifm
+
+                tens.shape = [shape[idx] for idx in reorder]
+                tens.bandwidth_shape = tens.shape
+                tens.storage_shape = tens.shape
+
+                if tens.values is not None:
+                    tens.values = tens.values.transpose(reorder)
+
+                op.ofm.values = tens.values
+                # Bypass the Transpose op
+                prev_op.set_output_tensor(op.ofm)
+                DebugDatabase.add_optimised(op, prev_op)
+                removed = True
+
+        if not removed:
+            print("Cannot remove Transpose, and handling of Transpose is not supported")
+            assert False
+
+    return op
+
+
+def remove_reshapes(op, arch):
+    if op.run_on_npu and op.type == Op.Reshape:
+        bypass_reshape_and_squeeze_ops(op)
+
+
 def rewrite_activation(op, arch, nng):
     if op.type not in (Op.ReluN, Op.Clamp):
         return op
@@ -206,6 +246,7 @@
 
 def supported_operator_check(op, arch, nng):
     op.run_on_npu = arch.tosa_supported_operators.is_operator_supported(op)
+    assert op.run_on_npu or op.type in (Op.Placeholder, Op.SubgraphInput, Op.Const)
     return op
 
 
@@ -221,8 +262,25 @@
             nng, sg, arch, [], pre_process_list, rewrite_unsupported=False,
         )
 
+    # Removal of Transpose
+    for idx, sg in enumerate(nng.subgraphs):
+        nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
+            nng, sg, arch, [], [remove_const_transpose], rewrite_unsupported=False,
+        )
+
+    # Handle sg input output
+    for idx, sg in enumerate(nng.subgraphs):
+        nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
+            nng, sg, arch, [], [fix_sg_input_output], rewrite_unsupported=False,
+        )
+
+    # Removal of reshapes
+    for sg in nng.subgraphs:
+        rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [remove_reshapes])
+        sg.refresh_after_modification()
+
     # Rewite Operators step
-    op_rewrite_list = [set_tensor_equivalence, rewrite_rescale]
+    op_rewrite_list = [set_tensor_equivalence, rewrite_rescale, convert_depthwise_to_conv]
 
     for idx, sg in enumerate(nng.subgraphs):
         nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
diff --git a/ethosu/vela/tosa_mapping.py b/ethosu/vela/tosa_mapping.py
index 75ca43e..5d0dd33 100644
--- a/ethosu/vela/tosa_mapping.py
+++ b/ethosu/vela/tosa_mapping.py
@@ -221,11 +221,9 @@
     TosaOp.REDUCE_SUM,
     TosaOp.CONCAT,
     TosaOp.PAD,
-    TosaOp.RESHAPE,
     TosaOp.REVERSE,
     TosaOp.SLICE,
     TosaOp.TILE,
-    TosaOp.TRANSPOSE,
     TosaOp.GATHER,
     TosaOp.SCATTER,
     TosaOp.RESIZE,
@@ -304,11 +302,11 @@
     # TODO TosaOp.REDUCE_SUM
     # TODO TosaOp.CONCAT
     # TODO TosaOp.PAD
-    # TODO TosaOp.RESHAPE
+    TosaOp.RESHAPE: (Op.Reshape, reshape_attrs, None, TOSA_IFM_INDICES),
     # TODO TosaOp.REVERSE
     # TODO TosaOp.SLICE
     # TODO TosaOp.TILE
-    # TODO TosaOp.TRANSPOSE
+    TosaOp.TRANSPOSE: (Op.Transpose, None, None, TOSA_IFM_IFM2_INDICES),
     # TODO TosaOp.GATHER
     # TODO TosaOp.SCATTER
     # TODO TosaOp.RESIZE
diff --git a/ethosu/vela/tosa_reader.py b/ethosu/vela/tosa_reader.py
index eb31716..268d43c 100644
--- a/ethosu/vela/tosa_reader.py
+++ b/ethosu/vela/tosa_reader.py
@@ -33,9 +33,11 @@
 from .tensor import shape_num_elements
 from .tensor import Tensor
 from .tflite_mapping import DataType
+from .tosa.Op import Op as TosaOp
 from .tosa.TosaGraph import TosaGraph as TG
 from .tosa_mapping import datatype_map
 from .tosa_mapping import datatype_map_numpy
+from .tosa_mapping import TOSA_IFM_INDICES
 from .tosa_mapping import tosa_operator_map
 from .tosa_mapping import unsupported_tosa_operators
 
@@ -89,7 +91,7 @@
         op_code = op_data.Op()
         if op_code in unsupported_tosa_operators:
             print("Unsupported Operator", op_code)
-            assert False
+            return
 
         op_type, attr_serializer, quant_serializer, indices = tosa_operator_map[op_code]
         inputs = []
@@ -104,6 +106,15 @@
             outputs.append(output_tens)
             assert output_tens is not None
 
+        # Permutation attribute for TRANSPOSE is an input tensor in TOSA
+        # TODO In order to optimise Depthwise spawning from TFLite Support for removing
+        # Transpose of constant data.
+        # Moving permutation to an attribute, to match internal graph representation for now
+        perms = None
+        if op_code == TosaOp.TRANSPOSE:
+            perms = perms = inputs.pop(1)
+            indices = TOSA_IFM_INDICES
+
         name = "unknown_op_name"
         if len(outputs):
             name = outputs[0].name
@@ -148,6 +159,7 @@
                 stride = op.attrs["stride"]
                 if len(stride) == 2:
                     op.attrs["strides"] = (1, stride[0], stride[1], 1)
+                    del op.attrs["stride"]
                 else:
                     # TODO CONV3D more to be done....
                     print("Unsupported kernel dimensions: ", len(stride))
@@ -167,6 +179,11 @@
                     # TODO CONV3D more to be done....
                     print("Unsupported kernel dimensions: ", len(kernel))
                     assert False
+            if op.type.is_depthwise_conv2d_op():
+                op.attrs["depth_multiplier"] = op.weights.shape[3]
+
+        elif op.type == Op.Transpose:
+            op.attrs["perms"] = perms.values
 
         if quant_serializer is not None:
             quant_info = quant_serializer.deserialize(op_data)
diff --git a/ethosu/vela/tosa_supported_operators.py b/ethosu/vela/tosa_supported_operators.py
index 3b0e6b3..90d5468 100644
--- a/ethosu/vela/tosa_supported_operators.py
+++ b/ethosu/vela/tosa_supported_operators.py
@@ -28,19 +28,24 @@
     # TODO currently sparsely populated
     # Categorised lists of supported operators
     convolution_ops = set((Op.Conv2DBias,))
-    convolution_like_ops = convolution_ops
+    depthwise_convolution_ops = set((Op.DepthwiseConv2DBias,))
+    convolution_like_ops = convolution_ops | depthwise_convolution_ops
+
+    # TODO depending on what will be committed
     max_pooling_ops = Op.op_set(Op.is_maxpool_op)
     avg_pooling_ops = Op.op_set(Op.is_avgpool_op)
-    pooling_ops = set((Op.ReduceSum,)) | max_pooling_ops | avg_pooling_ops
+    pooling_ops = max_pooling_ops | avg_pooling_ops
+    fc_vector_products = set((Op.FullyConnected,))
 
-    mac_main_ops = convolution_like_ops | pooling_ops
+    mac_main_ops = convolution_like_ops | pooling_ops | fc_vector_products
+    memory_only_ops = set((Op.Reshape, Op.Transpose,))
 
     type_conversion_ops = set((Op.Rescale,))
     relu_ops = set((Op.Clamp, Op.ReluN,))
     activation_ops = relu_ops
 
     npu_post_ops = activation_ops
-    supported_operators = mac_main_ops | type_conversion_ops | npu_post_ops
+    supported_operators = mac_main_ops | type_conversion_ops | npu_post_ops | memory_only_ops
 
     # Supported data types
     # TODO will differ compared to TensorFlow Lite, currently set to the same
@@ -54,6 +59,12 @@
         # Setup specific constraints. Note: the order matters
         self.specific_constraints = defaultdict(list)
 
+        self.specific_constraints[Op.Transpose].append(TosaSupportedOperators.constraint_ifm_producer)
+
+        # Depthwise Conv specific checks:
+        for op_type in TosaSupportedOperators.depthwise_convolution_ops:
+            self.specific_constraints[op_type].append(TosaSupportedOperators.constraint_depth_multiplier)
+
     def is_operator_supported(self, op):
         ext_type = optype_to_tosa_op_type(op.type)
         if op.type not in TosaSupportedOperators.supported_operators:
@@ -87,3 +98,25 @@
                 valid = False
                 extra.append(f"Tensor '{tens.name}' has data type: {tens.dtype}")
         return valid, ", ".join(extra)
+
+    @staticmethod
+    def constraint_ifm_producer(cls, op):
+        "Input must be constant data"
+        valid = op.ifm.ops and op.ifm.ops[0].type == Op.Const
+        return valid, "Op has ifm with non-constant data"
+
+    # TODO duplicates tflite_supported operators, but support for depth multiplier should be added at a later stage
+    @staticmethod
+    def constraint_depth_multiplier(op):
+        "For depth multipliers > 1, IFM channels must be 1 and OFM channels must be equal to the depth multiplier"
+        depth_multiplier = op.attrs.get("depth_multiplier", 1)
+        if depth_multiplier > 1:
+            ifm_channels = op.ifm.shape[3]
+            ofm_channels = op.ofm.shape[3]
+            valid = (ifm_channels == 1) and (ofm_channels == depth_multiplier)
+            extra = (
+                f"Op has ifm_channels={ifm_channels}, ofm_channels={ofm_channels}"
+                f" and depth_multiplier={depth_multiplier}"
+            )
+            return valid, extra
+        return True, "Op has depth_multiplier=1"
diff --git a/ethosu/vela/vela.py b/ethosu/vela/vela.py
index 9448749..6c9fbce 100644
--- a/ethosu/vela/vela.py
+++ b/ethosu/vela/vela.py
@@ -85,7 +85,7 @@
     )
 
     output_tfl_filename = output_basename + "_vela.tflite"
-    if input_name.endswith(".tflite"):
+    if input_name.endswith(".tflite") or input_name.endswith(".tosa"):
         tflite_writer.write_tflite(nng, output_tfl_filename)
     if input_name.endswith(".tosa"):
         rawdata_writer.write_rawdata_output(nng, arch, output_basename)