diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index 652136c82303d8b9ca6772f228312fe96efd33ea..46a97f6fab25029727fd7c106d978ff60130dab6 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -349,15 +349,15 @@ class InferStreamingMaxPool(Transformation):
         graph = model.graph
         node_ind = 0
         graph_modified = False
-        for n in graph.node:
+        for node in graph.node:
             node_ind += 1
-            if n.op_type == "MaxPoolNHWC":
-                mp_input = n.input[0]
-                mp_output = n.output[0]
+            if node.op_type == "MaxPoolNHWC":
+                mp_input = node.input[0]
+                mp_output = node.output[0]
                 mp_in_shape = model.get_tensor_shape(mp_input)
                 # mp_out_shape = model.get_tensor_shape(mp_output)
                 dt = model.get_tensor_datatype(mp_input)
-                mp_inst = getCustomOp(n)
+                mp_inst = getCustomOp(node)
                 k_h, k_w = mp_inst.get_nodeattr("kernel_shape")
                 ifm_ch = mp_in_shape[-1]
                 ifm_dim_h = mp_in_shape[1]
@@ -365,8 +365,11 @@ class InferStreamingMaxPool(Transformation):
                 pe = 1
                 ceil_mode = mp_inst.get_nodeattr("ceil_mode")
                 is_1d = (ifm_dim_h == 1 and k_h == 1) or (ifm_dim_w == 1 and k_w == 1)
-                is_divisable = ifm_dim_h % k_h == 0 or ifm_dim_w % k_w == 0
-                if is_1d or is_divisable:
+                is_divisable = (ifm_dim_h % k_h == 0) or (ifm_dim_w % k_w == 0)
+                is_bipolar = dt == DataType["BIPOLAR"]
+                pass_1d = is_1d and (not is_bipolar)
+                pass_2d = (not is_1d) and is_divisable
+                if pass_1d or pass_2d:
                     # create equivalent StreamingMaxPool_Batch node
                     new_node = helper.make_node(
                         "StreamingMaxPool_Batch",
@@ -380,12 +383,14 @@ class InferStreamingMaxPool(Transformation):
                         dataType=dt.name,
                         PE=pe,
                         CeilMode=ceil_mode,
-                        name="StreamingMaxPool_Batch_" + n.name,
+                        name="StreamingMaxPool_Batch_" + node.name,
                     )
                     graph.node.insert(node_ind, new_node)
                     # remove old nodes
-                    graph.node.remove(n)
+                    graph.node.remove(node)
                     graph_modified = True
+                else:
+                    warnings.warn(node.name + ": could not convert to HLS")
         if graph_modified:
             model = model.transform(InferShapes())
             model = model.transform(InferDataTypes())