diff --git a/tests/fpgadataflow/test_fpgadataflow_vvau.py b/tests/fpgadataflow/test_fpgadataflow_vvau.py
deleted file mode 100644
index 6fa3d3706d880ec66a3c8d19ca23d6381b0553c0..0000000000000000000000000000000000000000
--- a/tests/fpgadataflow/test_fpgadataflow_vvau.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# 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.core.datatype import DataType
-from finn.core.modelwrapper import ModelWrapper
-from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
-from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
-from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
-from finn.util.basic import gen_finn_dt_tensor
-
-
-def make_single_vvau_modelwrapper(W, pe, dim, ch, k, wdt, idt, odt, T=None, tdt=None):
-
-    inp = helper.make_tensor_value_info(
-        "inp", TensorProto.FLOAT, [1, dim, dim, k * k * ch]
-    )
-    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, dim, dim, ch])
-    if T is not None:
-        no_act = 0
-        node_inp_list = ["inp", "weights", "thresh"]
-        if odt == DataType.BIPOLAR:
-            actval = 0
-        else:
-            actval = odt.min()
-    else:
-        # no thresholds
-        node_inp_list = ["inp", "weights"]
-        actval = 0
-        no_act = 1
-
-    VVAU_node = helper.make_node(
-        "Vector_Vector_Activate_Batch",
-        node_inp_list,
-        ["outp"],
-        domain="finn",
-        backend="fpgadataflow",
-        resType="ap_resource_lut()",
-        PE=pe,
-        Dim=dim,
-        Channels=ch,
-        Kernel=k,
-        ActVal=actval,
-        inputDataType=idt.name,
-        weightDataType=wdt.name,
-        outputDataType=odt.name,
-        noActivation=no_act,
-    )
-    graph = helper.make_graph(
-        nodes=[VVAU_node], name="vvau-graph", inputs=[inp], outputs=[outp]
-    )
-
-    model = helper.make_model(graph, producer_name="vvau-model")
-    model = ModelWrapper(model)
-    model.set_tensor_datatype("inp", idt)
-    model.set_tensor_datatype("outp", odt)
-    model.set_tensor_datatype("weights", wdt)
-    model.set_initializer("weights", W)
-
-    if T is not None:
-        model.set_tensor_datatype("thresh", tdt)
-        model.set_initializer("thresh", T)
-    return model
-
-
-# pe
-@pytest.mark.parametrize("pe", [1, 2])  # , 4, 8])
-# input datatype
-@pytest.mark.parametrize("idt", [DataType.INT2, DataType.INT4])
-# weight datatype
-@pytest.mark.parametrize("wdt", [DataType.INT2, DataType.INT4])
-# kernel size
-@pytest.mark.parametrize("k", [2, 4])
-# dimension
-@pytest.mark.parametrize("dim", [4, 6])
-# channels
-@pytest.mark.parametrize("ch", [2])  # 2, 4])
-def test_fpgadataflow_vvau_cppsim(pe, idt, wdt, k, dim, ch):
-    odt = DataType.INT32
-    # generate weights
-    W = gen_finn_dt_tensor(wdt, (ch, 1, k, k))
-    model = make_single_vvau_modelwrapper(W, pe, dim, ch, k, wdt, idt, odt)
-    model = model.transform(SetExecMode("cppsim"))
-    model = model.transform(PrepareCppSim())
-    model = model.transform(CompileCppSim())
-    # generate inputs
-    x = gen_finn_dt_tensor(idt, (1, dim, dim, k * k * ch))
-
-    idict = {"inp": x}
-    y_produced = oxe.execute_onnx(model, idict)["outp"]
-
-    # test
-    W_sparse = np.zeros((ch, ch, k, k))
-    for c in range(ch):
-        W_sparse[c][c] = W[c][0]
-
-    if pe == 2:
-        W_sparse = W_sparse.transpose(0, 2, 3, 1)
-    W_sparse = W_sparse.reshape(ch, k * k * ch)
-    y_expected = np.matmul(x, W_sparse.T)
-
-    assert (y_produced == y_expected).all()