TadejMurovic/BNN_Deployment
Part of paper: Massively Parallel Combinational Binary Neural Networks for Edge Processing
This project helps operations engineers and researchers deploy specialized neural networks onto low-power hardware. It takes pre-trained binary neural network parameters and dataset binarization scripts as input, producing Verilog files for each network layer. The output can be directly integrated into FPGA synthesis projects like Vivado or Quartus for edge processing applications.
No commits in the last 6 months.
Use this if you need to transform pre-trained binary neural network models into hardware-ready Verilog descriptions for efficient deployment on edge devices.
Not ideal if you need to train binary neural networks from scratch or if your target hardware is not an FPGA.
Stars
12
Forks
2
Language
MATLAB
License
—
Category
Last pushed
Jun 27, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/TadejMurovic/BNN_Deployment"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
fastmachinelearning/hls4ml
Machine learning on FPGAs using HLS
alibaba/TinyNeuralNetwork
TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework.
KULeuven-MICAS/zigzag
HW Architecture-Mapping Design Space Exploration Framework for Deep Learning Accelerators
fastmachinelearning/hls4ml-tutorial
Tutorial notebooks for hls4ml
doonny/PipeCNN
An OpenCL-based FPGA Accelerator for Convolutional Neural Networks