diff --git a/src/finn/util/create.py b/src/finn/util/create.py
new file mode 100644
index 0000000000000000000000000000000000000000..853cdd0d44a05426b34bf1db3caa58d9289b2e9e
--- /dev/null
+++ b/src/finn/util/create.py
@@ -0,0 +1,178 @@
+# 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 numpy as np
+from finn.core.modelwrapper import ModelWrapper
+from onnx import TensorProto, helper
+from finn.core.datatype import DataType
+from finn.util.basic import calculate_signed_dot_prod_range, gen_finn_dt_tensor
+
+
+def hls_random_mlp_maker(layer_spec):
+ """Create an MLP of given specification using HLSCustomOp instances.
+ Generate random weights/thresholds of appropriate size."""
+ ret = []
+ for l in layer_spec:
+ idt = l["idt"]
+ wdt = l["wdt"]
+ mw = l["mw"]
+ mh = l["mh"]
+ act = l["act"]
+ l["W"] = gen_finn_dt_tensor(wdt, (mw, mh))
+ if act is None:
+ # no activation, produce accumulators
+ T = None
+ tdt = None
+ if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
+ odt = DataType.UINT32
+ else:
+ odt = DataType.INT32
+ else:
+ odt = act
+ (min, max) = calculate_signed_dot_prod_range(idt, wdt, mw)
+ n_steps = act.get_num_possible_values() - 1
+ T = np.random.randint(min, max - 1, (mh, n_steps)).astype(np.float32)
+ # provide non-decreasing thresholds
+ T = np.sort(T, axis=1)
+ # generate thresholds for activation
+ if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
+ tdt = DataType.UINT32
+ # bias thresholds to be positive
+ T = np.ceil((T + mw) / 2)
+ assert (T >= 0).all()
+ else:
+ tdt = DataType.INT32
+ l["T"] = T
+ l["tdt"] = tdt
+ l["odt"] = odt
+ ret.append(l)
+
+ return hls_mlp_maker(ret)
+
+
+def hls_mlp_maker(layer_spec):
+ """Create an MLP of given specification using HLSCustomOp instances."""
+
+ current_in_name = ""
+ current_out_name = ""
+ i = 0
+
+ graph = helper.make_graph(nodes=[], name="mlp", inputs=[], outputs=[])
+
+ model = helper.make_model(graph, producer_name="finn")
+ model = ModelWrapper(model)
+
+ for l in layer_spec:
+ current_W_name = "W_%d" % i
+ current_T_name = "T_%d" % i
+ current_in_name = "act_%d" % i
+ current_out_name = "act_%d" % (i + 1)
+
+ W = l["W"]
+ (mw, mh) = W.shape
+ T = l["T"]
+ pe = l["pe"]
+ simd = l["simd"]
+ wdt = l["wdt"]
+ idt = l["idt"]
+ tdt = l["tdt"]
+ odt = l["odt"]
+
+ if i == 0:
+ global_in = helper.make_tensor_value_info(
+ current_in_name, TensorProto.FLOAT, [1, mw]
+ )
+ model.graph.input.append(global_in)
+
+ if i == len(layer_spec) - 1:
+ global_out = helper.make_tensor_value_info(
+ current_out_name, TensorProto.FLOAT, [1, mh]
+ )
+ model.graph.output.append(global_out)
+
+ # there are two ways to implement bipolar weights and inputs for
+ # StreamingFC:
+ # - specify their datatypes as such
+ # - specify their datatypes as BINARY as use binaryXnorMode
+ if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
+ # we'll internally convert weights/inputs to binary and specify the
+ # datatypes as such, and also set the binaryXnorMode attribute to 1
+ export_wdt = DataType.BINARY
+ export_idt = DataType.BINARY
+ binary_xnor_mode = 1
+ else:
+ export_wdt = wdt
+ export_idt = idt
+ binary_xnor_mode = 0
+
+ if T is not None:
+ no_act = 0
+ node_inp_list = [current_in_name, current_W_name, current_T_name]
+ if odt == DataType.BIPOLAR:
+ actval = 0
+ else:
+ actval = odt.min()
+ else:
+ # no thresholds
+ node_inp_list = [current_in_name, current_W_name]
+ actval = 0
+ no_act = 1
+ FCLayer_node = helper.make_node(
+ "StreamingFCLayer_Batch",
+ node_inp_list,
+ [current_out_name],
+ domain="finn",
+ backend="fpgadataflow",
+ resType="ap_resource_lut()",
+ MW=mw,
+ MH=mh,
+ SIMD=simd,
+ PE=pe,
+ inputDataType=export_idt.name,
+ weightDataType=export_wdt.name,
+ outputDataType=odt.name,
+ ActVal=actval,
+ binaryXnorMode=binary_xnor_mode,
+ noActivation=no_act,
+ )
+
+ model.graph.node.append(FCLayer_node)
+ model.set_tensor_datatype(current_in_name, idt)
+ model.set_tensor_datatype(current_out_name, odt)
+ model.set_tensor_datatype(current_W_name, wdt)
+ if binary_xnor_mode:
+ # convert bipolar to binary
+ model.set_initializer(current_W_name, (W + 1) / 2)
+ else:
+ model.set_initializer(current_W_name, W)
+ if T is not None:
+ model.set_tensor_datatype(current_T_name, tdt)
+ model.set_initializer(current_T_name, T)
+ i += 1
+
+ return model
diff --git a/tests/util/test_create.py b/tests/util/test_create.py
new file mode 100644
index 0000000000000000000000000000000000000000..7173add35abf04a35c33b0ef10b42ffdb296a653
--- /dev/null
+++ b/tests/util/test_create.py
@@ -0,0 +1,64 @@
+# 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 finn.util.create as create
+from finn.core.datatype import DataType
+
+
+@pytest.mark.parametrize("bitwidth", [DataType.BIPOLAR, DataType.INT2, DataType.INT4])
+def test_hls_random_mlp_maker(bitwidth):
+ w = bitwidth
+ a = bitwidth
+ layer_spec = [
+ {
+ "mw": 185,
+ "mh": 100,
+ "simd": 185,
+ "pe": 100,
+ "idt": DataType.BIPOLAR,
+ "wdt": w,
+ "act": a,
+ },
+ {"mw": 100, "mh": 100, "simd": 100, "pe": 100, "idt": a, "wdt": w, "act": a},
+ {"mw": 100, "mh": 100, "simd": 100, "pe": 100, "idt": a, "wdt": w, "act": a},
+ {"mw": 100, "mh": 100, "simd": 100, "pe": 100, "idt": a, "wdt": w, "act": a},
+ {
+ "mw": 100,
+ "mh": 1,
+ "simd": 100,
+ "pe": 1,
+ "idt": a,
+ "wdt": w,
+ "act": DataType.BIPOLAR,
+ },
+ ]
+
+ ret = create.hls_random_mlp_maker(layer_spec)
+ assert len(ret.graph.node) == 5
+ ret.save("mlp-%s.onnx" % str(bitwidth))