diff --git a/src/finn/transformation/fpgadataflow/insert_dwc.py b/src/finn/transformation/fpgadataflow/insert_dwc.py
index b56c8b74e..2e3f0bf4d 100644
--- a/src/finn/transformation/fpgadataflow/insert_dwc.py
+++ b/src/finn/transformation/fpgadataflow/insert_dwc.py
@@ -67,7 +67,7 @@ def apply(self, model):
         for n in graph.node:
             node_ind += 1
             if _suitable_node(n):
-                for output_name in n.output:
+                for out_idx, output_name in enumerate(n.output):
                     consumers = model.find_consumers(output_name)
                     if consumers == []:
                         continue
@@ -78,39 +78,28 @@ def apply(self, model):
                     if _suitable_node(consumer) is True:
                         n0 = getCustomOp(n)
                         n1 = getCustomOp(consumer)
-                        n0_out_shape = n0.get_folded_output_shape()
-                        # in some special cases, we need to get folded shapes of
-                        # non-default inputs for the consumer
-                        # - if FC and external mem, it could be connected to input 1
-                        # - if concat, could be connected to any input
-                        if (
-                            consumer.op_type.startswith("MVAU")
-                            and n1.get_nodeattr("mem_mode") == "external"
-                        ) or (consumer.op_type.startswith("StreamingConcat")):
-                            # get input idx
-                            in_idx = None
-                            for idx, n_input in enumerate(consumer.input):
-                                if output_name == n_input:
-                                    in_idx = idx
-                            assert in_idx is not None, "Malformed model"
-                            n1_in_shape = n1.get_folded_input_shape(in_idx)
-                        else:
-                            # use default folded input shape
-                            n1_in_shape = n1.get_folded_input_shape()
+                        n0_out_shape = n0.get_folded_output_shape(out_idx)
+                        # get input idx
+                        in_idx = None
+                        for idx, n_input in enumerate(consumer.input):
+                            if output_name == n_input:
+                                in_idx = idx
+                        assert in_idx is not None, "Malformed model"
+                        n1_in_shape = n1.get_folded_input_shape(in_idx)
 
                         if n0_out_shape[-1] != n1_in_shape[-1]:
                             graph_modified = True
                             # determine dwc inwidth
-                            dwc_in_width = n0.get_outstream_width()
+                            dwc_in_width = n0.get_outstream_width(out_idx)
                             # determine dwc outwidth
-                            dwc_out_width = n1.get_instream_width()
+                            dwc_out_width = n1.get_instream_width(in_idx)
                             node_optype = "StreamingDataWidthConverter"
 
                             # determine shape for dwc
-                            dwc_shape = n0.get_normal_output_shape()
+                            dwc_shape = n0.get_normal_output_shape(out_idx)
 
                             # determine FINN dtype for dwc
-                            dtype = n0.get_output_datatype()
+                            dtype = n0.get_output_datatype(out_idx)
                             # determine onnx tensor dtype for dwc
                             n0_otensor = model.get_tensor_valueinfo(output_name)
                             n0_tensor_dtype = n0_otensor.type.tensor_type.elem_type