diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index d0d3d7c..13f08f2 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -317,7 +317,7 @@
     return op
 
 
-def convert_batched_fc_to_conv(op, arch, nng):
+def convert_batched_fc_shape(op, arch, nng):
     if op.type == Op.FullyConnected:
         ifm = op.inputs[0]
         ofm = op.outputs[0]
@@ -327,20 +327,6 @@
             batching_split = {4: (2, 2), 8: (2, 4), 16: (4, 4)}
             h, w = batching_split.get(n, (1, n))
 
-            # Convert to convolution
-            op.name += "_conv"
-            op.type = Op.Conv2DBias
-            op.attrs = {
-                "dilation": (1, 1, 1, 1),
-                "dilation_h_factor": 1,
-                "dilation_w_factor": 1,
-                "padding": b"SAME",
-                "stride_h": 1,
-                "stride_w": 1,
-                "strides": (1, 1, 1, 1),
-                "is_converted_fc": True,
-            }
-
             prev_op = ifm.ops[0]
             desired_shape = [1, h, w, ifm.shape[-1]]
             if len(ifm.consumer_list) == 1 and prev_op is not None and prev_op.type == Op.Reshape:
@@ -381,7 +367,7 @@
                 else:
                     op.outputs[0].set_all_shapes(desired_shape)
             else:
-                # Add rehape op to the output
+                # Add reshape op to the output
                 op.set_output_tensor(create_reshape_tensor(ofm, desired_shape, False))
     return op
 
@@ -1096,7 +1082,7 @@
         convert_conv_to_fc,
         convert_softmax,
         fixup_fully_connected_input,
-        convert_batched_fc_to_conv,
+        convert_batched_fc_shape,
         fixup_pack_input,
         unfuse_activation_function,
         fixup_conv2d_backprop,
diff --git a/ethosu/vela/insert_dma.py b/ethosu/vela/insert_dma.py
index f02039c..fc1e798 100644
--- a/ethosu/vela/insert_dma.py
+++ b/ethosu/vela/insert_dma.py
@@ -77,9 +77,7 @@
                 ):
                     only_vector_product_consumers = True
                     for oper in tens.consumers():
-                        if oper is None or not (
-                            oper.type.npu_block_type == NpuBlockType.VectorProduct or "is_converted_fc" in oper.attrs
-                        ):
+                        if oper is None or oper.type.npu_block_type != NpuBlockType.VectorProduct:
                             only_vector_product_consumers = False
                             break
 
diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py
new file mode 100644
index 0000000..62a1b76
--- /dev/null
+++ b/ethosu/vela/test/test_graph_optimiser.py
@@ -0,0 +1,61 @@
+# Copyright (C) 2020 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 graph_optimiser
+import numpy as np
+
+from ethosu.vela.graph_optimiser import convert_batched_fc_shape
+from ethosu.vela.operation import Op
+from ethosu.vela.tensor import create_const_tensor
+from ethosu.vela.tensor import Tensor
+from ethosu.vela.test import testutil
+
+
+def test_convert_batched_fc():
+    """Tests shape conversion of batched fully connected"""
+    shape = [4, 8]
+    ifm = create_const_tensor("test_in", shape, np.uint8, np.zeros(shape))
+    weights = create_const_tensor("weight_in", shape, np.uint8, np.zeros(shape))
+    ofm = Tensor(ifm.shape, np.uint8, "test_out")
+    op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm)
+    ifm.consumer_list.append(op)
+
+    prev_op = op.clone()
+    conv_op = convert_batched_fc_shape(op, None, None)
+
+    assert conv_op.ifm != prev_op.ifm
+    assert conv_op.ofm != prev_op.ofm
+    assert conv_op.type == Op.FullyConnected
+    assert len(conv_op.ifm.shape) == 4
+    assert conv_op.ifm.shape == conv_op.ofm.shape
+    assert conv_op.ifm.ops[0].type == Op.Reshape
+
+    shape = [1, 8]
+    ifm.shape = shape
+    weights.shape = shape
+    ofm.shape = shape
+    op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm)
+    ifm.consumer_list.append(op)
+
+    prev_op = op.clone()
+    conv_op = convert_batched_fc_shape(op, None, None)
+
+    assert conv_op.ifm == prev_op.ifm
+    assert conv_op.ofm == prev_op.ofm
+    assert conv_op.type == Op.FullyConnected
+    assert len(conv_op.ifm.shape) == 2
+    assert conv_op.ifm.shape == conv_op.ofm.shape