diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index e8218fc..bea22a2 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -1435,12 +1435,46 @@
                 # This attribute means a different scaling calculation is used in order to match reference
                 op.low_precision_scaling = True
                 weight_scale = h * w
+                # Set zero points to 0 as they will be adjusted for with bias term
                 foq = ofmq.clone()
-                foq.zero_point -= int(np.round(ifmq.zero_point * ifmq.scale_f32 / foq.scale_f32))
-                op.forced_output_quantization = foq
+                foq.zero_point = 0
                 fiq = ifmq.clone()
                 fiq.zero_point = 0
                 op.forced_input_quantization = fiq
+                bias_term = ofmq.zero_point - int(ifmq.zero_point * ifmq.scale_f32 / ofmq.scale_f32)
+                # If the bias term is outside uint8 range, we need an Add op to apply it.
+                if bias_term < 0 or bias_term > 255:
+                    intermediate = op.ofm.clone(suffix="_intermediate", set_unique=True)
+                    # Bias term has higher bitness (i32) than input/output (u8).
+                    # 16 bits is enough since the bias is added/subtracted from a u8 value,
+                    # the bias can only effectively assume values in the range [-255, 255].
+                    intermediate.dtype = DataType.int16
+                    intermediate.quantization.zero_point = 0
+                    add_op = Operation(Op.Add, op.name + "_bias")
+                    add_op.forced_output_quantization = foq
+                    add_op.add_input_tensor(intermediate)
+                    quant = QuantizationParameters()
+                    quant.zero_point = 0
+                    bias_term_tens = create_const_tensor(
+                        op.name + "_bias",
+                        [1, 1, 1, 1],
+                        DataType.int16,
+                        [bias_term],
+                        np.int16,
+                        quantization=quant,
+                        quant_value_dtype=np.int16,
+                    )
+                    add_op.add_input_tensor(bias_term_tens)
+                    add_op.set_output_tensor(op.ofm)
+                    add_op.set_ifm_ofm_shapes()
+                    add_op.activation = op.activation
+                    op.activation = None
+                    op.set_output_tensor(intermediate)
+                    op.set_ifm_ofm_shapes()
+                # If not, we can just do it with the OFM zero point.
+                else:
+                    foq.zero_point = bias_term
+                    op.forced_output_quantization = foq
             else:
                 assert inp.dtype == DataType.int8
                 # Use a depthwise to calculate the sum,