binary-neural-networks

Efficient computing methods developed by Huawei Noah's Ark Lab

#计算机科学#(New version is out: https://github.com/hpi-xnor/BMXNet-v2) BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet

#计算机科学#BMXNet 2: An Open-Source Binary Neural Network Implementation Based on MXNet

[CVPR 2020] This project is the PyTorch implementation of our accepted CVPR 2020 paper : forward and backward information retention for accurate binary neural networks.

#计算机科学#Binary Neural Network Framework for FPGA(Differentiable LUT)

#计算机科学#BinaryNets in TensorFlow with XNOR GEMM op

This project is the official implementation of 'Basic Binary Convolution Unit for Binarized Image Restoration Network', ICLR2023

Pytorch implementation of our paper accepted by NeurIPS 2020 -- Rotated Binary Neural Network

This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

Implemented here a Binary Neural Network (BNN) achieving nearly state-of-art results but recorded a significant reduction in memory usage and total time taken during training the network.

The collection of training tricks of binarized neural networks.

S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)

[CVPRW 21] "BNN - BN = ? Training Binary Neural Networks without Batch Normalization", Tianlong Chen, Zhenyu Zhang, Xu Ouyang, Zechun Liu, Zhiqiang Shen, Zhangyang Wang

[ICML 2023] This project is the official implementation of our accepted ICML 2023 paper BiBench: Benchmarking and Analyzing Network Binarization.

PyTorch implementation of Local Binary Convolutional Neural Network http://xujuefei.com/lbcnn.html

PyTorch implementation of binary neural networks

#计算机科学#BITorch: Open-Source Implementation of Binary Neural Networks with PyTorch

System Verilog code describing a fully combinational binarized neural network.

#计算机科学#Pytorch implementation of BiFSMNv2, TNNLS 2023

[ICCV 2021] Code release for "Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks"