diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index 4696446..32f97d2 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -420,54 +420,73 @@
     return op
 
 
+def fixup_stridedslice_output(tens, arch, nng):
+    op = tens.ops[0]
+    if op.type == Op.StridedSlice:
+        reshape_input_shape = tens.shape
+        new_axis_mask = op.attrs["new_axis_mask"]
+        shrink_axis_mask = op.attrs["shrink_axis_mask"]
+        ellipsis_mask = op.attrs["ellipsis_mask"]
+
+        if (new_axis_mask != 0 and shrink_axis_mask != 0) or ellipsis_mask != 0:
+            # Not supported, will be put on CPU
+            return tens
+        if shrink_axis_mask == 0 and new_axis_mask == 0:
+            # Equal Rank StridedSlice, no need to insert reshape
+            return tens
+        elif shrink_axis_mask != 0:
+            n = 0
+            axis = 0
+            while shrink_axis_mask:
+                prev_mask = shrink_axis_mask
+                n += 1
+                shrink_axis_mask &= shrink_axis_mask - 1
+                axis = int(math.log2(prev_mask - shrink_axis_mask))
+                reshape_input_shape = reshape_input_shape[:axis] + [1] + reshape_input_shape[axis:]
+
+            assert len(tens.shape) == (len(op.inputs[0].shape) - n)
+            op.attrs["shrink_axis_mask"] = 0
+
+        elif new_axis_mask != 0:
+            n = 0
+            axis = 0
+            while new_axis_mask:
+                prev_mask = new_axis_mask
+                n += 1
+                new_axis_mask &= new_axis_mask - 1
+                axis = int(math.log2(prev_mask - new_axis_mask))
+                reshape_input_shape = reshape_input_shape[:axis] + reshape_input_shape[(axis + 1) :]
+                new_axis_mask >>= 1
+
+            assert len(tens.shape) == (len(op.inputs[0].shape) + n)
+            op.attrs["new_axis_mask"] = 0
+
+        # Construct 1 shape tensor to be used by all inserted reshape ops
+        new_shape_tens = create_const_tensor(op.name + "_reshape_shape", [1], DataType.int32, tens.shape)
+
+        for idx, out_tens in enumerate(op.outputs):
+            reshape_in = out_tens.clone("_reshaped")
+            reshape_in.set_all_shapes(reshape_input_shape)
+            reshape_in.ops = [op]
+
+            reshape_op = Operation(Op.Reshape, "{}{}_reshape".format(op.name, idx))
+            reshape_op.attrs["new_shape"] = reshape_input_shape
+            reshape_op.inputs = [reshape_in, new_shape_tens]
+            reshape_op.set_output_tensor(out_tens)
+
+            op.outputs[idx] = reshape_in
+
+    return tens
+
+
 def fixup_unpack_output(tens, arch, nng):
     op = tens.ops[0]
-    if op.run_on_npu and op.type in set((Op.Unpack, Op.StridedSlice)):
+    if op.run_on_npu and op.type == Op.Unpack:
         # Unpack is also referred to as Unstack
         # Requires the rewrite_split function to be called on the op afterwards
-
-        reshape_input_shape = tens.shape
-        if op.type == Op.StridedSlice:
-            new_axis_mask = op.attrs["new_axis_mask"]
-            shrink_axis_mask = op.attrs["shrink_axis_mask"]
-            ellipsis_mask = op.attrs["ellipsis_mask"]
-
-            if (new_axis_mask != 0 and shrink_axis_mask != 0) or ellipsis_mask != 0:
-                # Not supported, will be put on CPU
-                return tens
-            if shrink_axis_mask == 0 and new_axis_mask == 0:
-                # Equal Rank StridedSlice, no need to insert reshape
-                return tens
-            elif shrink_axis_mask != 0:
-                n = 0
-                axis = 0
-                while shrink_axis_mask:
-                    prev_mask = shrink_axis_mask
-                    n += 1
-                    shrink_axis_mask &= shrink_axis_mask - 1
-                    axis = int(math.log2(prev_mask - shrink_axis_mask))
-                    reshape_input_shape = reshape_input_shape[:axis] + [1] + reshape_input_shape[axis:]
-
-                assert len(tens.shape) == (len(op.inputs[0].shape) - n)
-                op.attrs["shrink_axis_mask"] = 0
-
-            elif new_axis_mask != 0:
-                n = 0
-                axis = 0
-                while new_axis_mask:
-                    prev_mask = new_axis_mask
-                    n += 1
-                    new_axis_mask &= new_axis_mask - 1
-                    axis = int(math.log2(prev_mask - new_axis_mask))
-                    reshape_input_shape = reshape_input_shape[:axis] + reshape_input_shape[(axis + 1) :]
-                    new_axis_mask >>= 1
-
-                assert len(tens.shape) == (len(op.inputs[0].shape) + n)
-                op.attrs["new_axis_mask"] = 0
-        else:
-            axis = int(op.attrs["axis"])
-            op.type = Op.UnpackReshaped
-            reshape_input_shape = tens.shape[:axis] + [1] + tens.shape[axis:]
+        axis = int(op.attrs["axis"])
+        op.type = Op.UnpackReshaped
+        reshape_input_shape = tens.shape[:axis] + [1] + tens.shape[axis:]
 
         # Construct 1 shape tensor to be used by all inserted reshape ops
         new_shape_tens = create_const_tensor(op.name + "_reshape_shape", [1], DataType.int32, tens.shape)
@@ -1073,7 +1092,7 @@
     for idx, sg in enumerate(nng.subgraphs):
         # rewrite graph pass
         nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(
-            nng, sg, arch, [], op_rewrite_list, rewrite_unsupported=False,
+            nng, sg, arch, [fixup_stridedslice_output], op_rewrite_list, rewrite_unsupported=False,
         )
 
     for idx, sg in enumerate(nng.subgraphs):