diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index 7b8a1bf6b83175cfda041cfc49a22273fd696d8e..3029e09d48e2157a2e5d229119b02214b91d8538 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -40,10 +40,6 @@ from qonnx.transformation.infer_shapes import InferShapes
 from qonnx.util.basic import get_by_name
 from qonnx.util.onnx import nchw_to_nhwc
 
-from finn.transformation.fpgadataflow.minimize_accumulator_width import (
-    MinimizeAccumulatorWidth,
-)
-
 
 class InferConvInpGen(Transformation):
     """Convert Im2Col layers to ConvolutionInputGenerator layers."""
@@ -761,7 +757,6 @@ class InferBinaryMatrixVectorActivation(Transformation):
                     graph.node.remove(n)
                     graph_modified = True
         if graph_modified:
-            model = model.transform(MinimizeAccumulatorWidth())
             model = model.transform(InferShapes())
             model = model.transform(InferDataTypes())
         return (model, graph_modified)
@@ -904,7 +899,6 @@ class InferQuantizedMatrixVectorActivation(Transformation):
                         graph.node.remove(n)
                         graph_modified = True
         if graph_modified:
-            model = model.transform(MinimizeAccumulatorWidth())
             model = model.transform(InferShapes())
             model = model.transform(InferDataTypes())
         return (model, graph_modified)
@@ -1057,7 +1051,6 @@ class InferVectorVectorActivation(Transformation):
                         graph.node.remove(n)
                         graph_modified = True
         if graph_modified:
-            model = model.transform(MinimizeAccumulatorWidth())
             model = model.transform(InferShapes())
             model = model.transform(InferDataTypes())
         return (model, graph_modified)
@@ -1135,7 +1128,7 @@ class InferThresholdingLayer(Transformation):
                     PE=pe,
                     numSteps=thl_thres_shape[1],
                     inputDataType=idt.name,
-                    weightDataType=idt.name,  # will be set by MinimizeAccumulatorWidth
+                    weightDataType=idt.name,  # can be tightened by MinimizeAccumulatorWidth
                     outputDataType=odt.name,
                     numInputVectors=list(thl_in_shape[:-1]),
                     ActVal=actval,
@@ -1148,7 +1141,6 @@ class InferThresholdingLayer(Transformation):
                 graph_modified = True
 
         if graph_modified:
-            model = model.transform(MinimizeAccumulatorWidth())
             model = model.transform(InferShapes())
             model = model.transform(InferDataTypes())
         return (model, graph_modified)