diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index b29a382..fdd6fc6 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -283,7 +283,7 @@
         if "Conv" in op.type:
             kernel_size = op.inputs[1].shape[:2]
             input_shape = op.inputs[0].shape
-        elif "Pool" in op.type:
+        elif "Pool" in op.type or "ResizeBilinear" == op.type:
             kernel_size = op.attrs["ksize"][1:3]
             input_shape = op.inputs[0].shape
         elif op.type == "ExtractImagePatches":
@@ -314,7 +314,7 @@
     )
 )
 depthwise_op = set(("DepthwiseConv2dNative", "DepthwiseConv2dBiasAct",))
-pool_op = set(("AvgPool", "MaxPool", "QuantizedAvgPool", "QuantizedMaxPool", "AvgPoolAct", "MaxPoolAct"))
+pool_op = set(("AvgPool", "MaxPool", "QuantizedAvgPool", "QuantizedMaxPool", "AvgPoolAct", "MaxPoolAct", "ResizeBilinear",))
 elementwise_op = set(("AddAct", "MulAct", "SubAct", "Maximum", "Minimum", "LeakyRelu", "Abs"))
 activation_ops = set(("Relu", "Relu6", "ReluN1To1", "Sigmoid", "Tanh"))
 memory_only_ops = set(("Reshape",))
diff --git a/ethosu/vela/pass_packing.py b/ethosu/vela/pass_packing.py
index bae8151..1ad5b4f 100644
--- a/ethosu/vela/pass_packing.py
+++ b/ethosu/vela/pass_packing.py
@@ -67,6 +67,8 @@
         "MaxPool",
         "AvgPoolAct",
         "MaxPoolAct",
+        # deconvolution
+        "ResizeBilinear",
     )
 )
 
diff --git a/ethosu/vela/register_command_stream_generator.py b/ethosu/vela/register_command_stream_generator.py
index 460cf01..7a4faa8 100644
--- a/ethosu/vela/register_command_stream_generator.py
+++ b/ethosu/vela/register_command_stream_generator.py
@@ -401,6 +401,8 @@
             use_global_scale = False
             # Specifies type of rounding to be used.
             rounding_mode = rounding.TFL
+            if primary_op.type == 'ResizeBilinear':
+                rounding_mode = rounding.TRUNCATE
             fmf = primary_op.attrs.get("fused_memory_function", None)
             faf = primary_op.attrs.get("fused_activation_function", None)
 
@@ -537,7 +539,11 @@
 
             emit.cmd0_with_param(cmd0.NPU_SET_ACC_FORMAT, acc_format_map[shared_buffer.use_accumulator_element])
 
-            emit.cmd0_with_param(cmd0.NPU_SET_IFM_UPSCALE, 0)
+            if primary_op.type == 'ResizeBilinear':
+                # perform nearest neighbor upscale
+                emit.cmd0_with_param(cmd0.NPU_SET_IFM_UPSCALE, 1)
+            else:
+                emit.cmd0_with_param(cmd0.NPU_SET_IFM_UPSCALE, 0)
 
             if npu_block_type in set(
                 (NpuBlockType.ConvolutionMxN, NpuBlockType.ConvolutionDepthWise, NpuBlockType.Pooling)
@@ -579,7 +585,7 @@
 
                     valid_padding = sum(explicit_padding) == 0
 
-                    if primary_op.type in set(("AvgPool", "AvgPoolAct")) and valid_padding:
+                    if primary_op.type in set(("AvgPool", "AvgPoolAct", "ResizeBilinear")) and valid_padding:
                         # For valid padding vela has to output scaling values
                         if faf == "Sigmoid" or faf == "Tanh":
                             rescale = 0x3000 * cmd.ifm_tensor.quantization.scale_f32
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index 1a25887..7334fe2 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -44,6 +44,8 @@
             | self.fc_vector_products
             # RNN/LSTM/GRU
             | set(("BlockLSTM"))
+            # deconvolution
+            | set(("ResizeBilinear",))
         )
         self.unary_elem_wise_main_ops = set(("LeakyRelu", "Abs"))
         self.binary_elem_wise_main_ops = set(
diff --git a/ethosu/vela/tflite_reader.py b/ethosu/vela/tflite_reader.py
index 4456d5a..aa0ec4d 100644
--- a/ethosu/vela/tflite_reader.py
+++ b/ethosu/vela/tflite_reader.py
@@ -156,6 +156,22 @@
         if opt_serializer is not None:
             op.attrs = opt_serializer.deserialize(op_data.BuiltinOptions(), op_data.CustomOptionsAsNumpy())
 
+            if op_type.startswith("ResizeBilinear"):
+                upscaled_shape = [op.inputs[0].shape[1] * 2, op.inputs[0].shape[2] * 2]
+                out_shape = op.outputs[0].shape[1:3]
+                if not op.attrs['align_corners'] and out_shape == upscaled_shape:
+                    # this means the output is supposed to be a x2 upscale,
+                    # so we need to do SAME padding
+                    op.attrs.update({'padding': b'SAME'})
+                elif (op.attrs['align_corners']
+                    and out_shape == [upscaled_shape[0] - 1, upscaled_shape[1] - 1]):
+                    # here we can just run the avg pool without padding and
+                    # produce a (M * 2 - 1, N * 2 - 1) sized output
+                    op.attrs.update({'padding': b'VALID'})
+                else:
+                    assert False, "Only 2x upscaling is supported"
+                op.attrs.update({'filter_width': 2, 'filter_height': 2, 'stride_w': 1, 'stride_h': 1,})
+
             if "stride_w" in op.attrs:
                 op.attrs["strides"] = (1, op.attrs["stride_h"], op.attrs["stride_w"], 1)
             if "filter_width" in op.attrs: