[ConvertToHLS] fix conversion for bipolar outputs

src/finn/transformation/fpgadataflow/convert_to_hls_layers.py

@@ -566,7 +566,7 @@ class InferQuantizedStreamingFCLayer(Transformation):
                     wmem = mw * mh // (pe * simd)
                     assert (
                         mw * mh == wmem * pe * simd
-                    ), """Requirement (MW * MH) divisiable by
+                    ), """Requirement (MW * MH) divisible by
                     (WMEM * PE * SIMD) is violated."""
                     # see if we have any following thresholds
                     consumer = model.find_consumer(mm_output)
@@ -583,20 +583,27 @@ class InferQuantizedStreamingFCLayer(Transformation):
                         thresholds neither 1 nor MH."""
                         odt = model.get_tensor_datatype(mt_output)
                         scale = getCustomOp(consumer).get_nodeattr("out_scale")
-                        bipolar_ok = odt == DataType.BIPOLAR and scale == 2.0
-                        assert (
-                            scale == 1.0 or bipolar_ok
-                        ), "out_scale must be equal to 1.0 for HLS conversion."
                         actval = getCustomOp(consumer).get_nodeattr("out_bias")
                         assert (
                             int(actval) == actval
                         ), "out_bias must be integer for HLS conversion."
                         actval = int(actval)
+                        odt_is_bipolar = odt == DataType.BIPOLAR
+                        bipolar_ok = (
+                            odt_is_bipolar and (scale == 2.0) and (actval == -1)
+                        )
+                        assert (
+                            scale == 1.0 or bipolar_ok
+                        ), "out_scale = 1.0 or bipolar output needed for conversion."
                         assert (not odt.signed()) or (
                             actval < 0
                         ), "Signed output requres actval < 0"
                         model.set_tensor_shape(mm_input, mm_in_shape)
                         model.set_tensor_shape(mt_output, mt_out_shape)
+                        if bipolar_ok:
+                            # remove bias for bipolar, since
+                            # binary->bipolar is achieved by reinterpretation
+                            actval = 0
                         # create and insert new StreamingFCLayer node
                         new_node = helper.make_node(
                             "StreamingFCLayer_Batch",