Part of paper: Massively Parallel Combinational Binary Neural Networks for Edge Processing
If using these files and algorithms please reference “T. Murovič, A. Trost, Massively Parallel Combinational Binary Neural Networks for Edge Processing, Elektrotehniški vestnik, vol. 86, no. 1-2, pp. 47-53, 2019”.
Researchgate link: https://www.researchgate.net/publication/333563328_Massively_parallel_combinational_binary_neural_networks_for_edge_processing
Paper link: https://ev.fe.uni-lj.si/1-2-2019/Murovic.pdf
cybersecurity_dataset.unswb15.m, hep_dataset_susy.m, imaging_dataset_mnist.m and iot_dataset_uji.m are binarization scripts for datasets referenced in the mentioned paper. The algorithms transform multi-modal notation of datasets to purely binary features and labels.
Datasets are also available in references from the paper and at 
as well.
.
Transformed datasets serve as inputs to binary neural networks training software by “M. Courbariaux, “Binary net.”
https://github.com/MatthieuCourbariaux/BinaryNet, 2016”. This software trains and produces network parameters for the desired dataset. As this parameters are still in the form of [-1 / 1] for weights or signed integer for biases the procedure from “Y. Umuroglu, N. J. Fraser, G. Gambardella, M. Blott, P. H. W.
Leong, M. Jahre, and K. A. Vissers, “Finn: A framework for fast,
scalable binarized neural network inference,” in FPGA, 2017” is used to transform this values to binary 0 and 1 and unsigned integers.
Subfolders include dump.txt files which are the already transformed weights and thresholds/biases for each layer of each dataset. In addition model.txt files are Verilog files of combinational circuits for each layer of a network. These can be directly copied into your Vivado or Quartus synthesis project.