diff --git a/docker/finn_entrypoint.sh b/docker/finn_entrypoint.sh
index fcd6f9d788d0af1cad6de5259e5e181e76ac96bc..3a1554ac13902f7ca82cd3d11f55373a0fd6ea54 100644
--- a/docker/finn_entrypoint.sh
+++ b/docker/finn_entrypoint.sh
@@ -15,7 +15,7 @@ gecho () {
# the repos themselves are cloned in the Dockerfile
BREVITAS_COMMIT=989cdfdba4700fdd900ba0b25a820591d561c21a
CNPY_COMMIT=4e8810b1a8637695171ed346ce68f6984e585ef4
-HLSLIB_COMMIT=13e9b0772a27a3a1efc40c878d8e78ed09efb716
+HLSLIB_COMMIT=afcfe75f3404249bddeeb3f15df65bd1fcb1072e
PYVERILATOR_COMMIT=1d89cb0d4e0c97469cc6352c611f876ec13edfa6
PYNQSHELL_COMMIT=0c82a61b0ec1a07fa275a14146233824ded7a13d
diff --git a/tests/fpgadataflow/test_fpgadataflow_channelwise_ops.py b/tests/fpgadataflow/test_fpgadataflow_channelwise_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..05d8e28498316f0a76da83cd70611801fdb37846
--- /dev/null
+++ b/tests/fpgadataflow/test_fpgadataflow_channelwise_ops.py
@@ -0,0 +1,152 @@
+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import pytest
+
+import numpy as np
+from onnx import TensorProto, helper
+
+import finn.core.onnx_exec as oxe
+from finn.analysis.fpgadataflow.hls_synth_res_estimation import hls_synth_res_estimation
+from finn.core.datatype import DataType
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+from finn.transformation.general import GiveUniqueNodeNames
+from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
+from finn.util.basic import gen_finn_dt_tensor
+from finn.transformation.fpgadataflow.replace_verilog_relpaths import (
+ ReplaceVerilogRelPaths,
+)
+
+
+def make_modelwrapper(C, pe, idt, odt, func, vecs):
+ NumChannels = C.shape[0]
+
+ inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, vecs + [NumChannels])
+ outp = helper.make_tensor_value_info(
+ "outp", TensorProto.FLOAT, vecs + [NumChannels]
+ )
+
+ node_inp_list = ["inp", "const"]
+
+ node = helper.make_node(
+ "ChannelwiseOp_Batch",
+ node_inp_list,
+ ["outp"],
+ domain="finn",
+ backend="fpgadataflow",
+ NumChannels=NumChannels,
+ Func=func,
+ PE=pe,
+ inputDataType=idt.name,
+ outputDataType=odt.name,
+ numInputVectors=vecs,
+ )
+ graph = helper.make_graph(nodes=[node], name="graph", inputs=[inp], outputs=[outp],)
+
+ model = helper.make_model(graph, producer_name="model")
+ model = ModelWrapper(model)
+
+ model.set_tensor_datatype("inp", idt)
+ model.set_tensor_datatype("outp", odt)
+
+ model.set_tensor_datatype("const", idt)
+ model.set_initializer("const", C)
+ return model
+
+
+# activation: None or DataType
+@pytest.mark.parametrize("act", [DataType.INT8])
+# input datatype
+@pytest.mark.parametrize("idt", [DataType.INT4])
+# folding, -1 is maximum possible
+@pytest.mark.parametrize("nf", [-1, 2, 1])
+# number of input features
+@pytest.mark.parametrize("ich", [16])
+# vecs
+@pytest.mark.parametrize("vecs", [[1], [1, 7, 7]])
+# function
+@pytest.mark.parametrize("func", ["add", "mul"])
+# execution mode
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+@pytest.mark.vivado
+@pytest.mark.slow
+def test_fpgadataflow_addmul(idt, act, nf, ich, func, vecs, exec_mode):
+ if nf == -1:
+ nf = ich
+ pe = ich // nf
+ assert ich % pe == 0
+
+ # generate input data
+ x = gen_finn_dt_tensor(idt, tuple(vecs + [ich]))
+
+ odt = act
+ C = np.random.randint(idt.min(), idt.max() + 1, ich).astype(np.float32)
+
+ model = make_modelwrapper(C, pe, idt, odt, func, vecs)
+
+ if exec_mode == "cppsim":
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
+ elif exec_mode == "rtlsim":
+ model = model.transform(SetExecMode("rtlsim"))
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(PrepareIP("xc7z020clg400-1", 5))
+ model = model.transform(HLSSynthIP())
+ model = model.transform(ReplaceVerilogRelPaths())
+ model = model.transform(PrepareRTLSim())
+ else:
+ raise Exception("Unknown exec_mode")
+
+ # package input data as dictionary
+ input_dict = {"inp": x}
+
+ oshape = model.get_tensor_shape("outp")
+
+ C_reshaped = np.broadcast_to(C.flatten(), x.shape)
+ if func == "add":
+ y = x + C_reshaped
+ elif func == "mul":
+ y = x * C_reshaped
+
+ y_expected = y.reshape(oshape)
+ # execute model
+ y_produced = oxe.execute_onnx(model, input_dict)["outp"]
+
+ y_produced = y_produced.reshape(y_expected.shape)
+
+ assert (y_produced == y_expected).all(), "cppsim failed"
+
+ if exec_mode == "rtlsim":
+ hls_synt_res_est = model.analysis(hls_synth_res_estimation)
+ assert "ChannelwiseOp_Batch_0" in hls_synt_res_est