# A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network

Source: [A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network](<https://www.sota2.com/research/paper/68fa72761e209131cea5683e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-25T05:00:34.132Z

Authors: Ruichen Ma, Guanchao Qiao, Yian Liu, Liwei Meng, Ning Ning, Yang Liu, Shaogang Hu

Published: 2024-03-06T14:28:49.000Z

## Abstract

Binary neural networks utilize 1-bit quantized weights and activations to reduce both the model's storage demands and computational burden. However, advanced binary architectures still incorporate millions of inefficient and nonhardware-friendly full-precision multiplication operations. A&B BNN is proposed to directly remove part of the multiplication operations in a traditional BNN and replace the rest with an equal number of bit operations, introducing the mask layer and the quantized RPReLU structure based on the normalizer-free network architecture. The mask layer can be removed during inference by leveraging the intrinsic characteristics of BNN with straightforward mathematical transformations to avoid the associated multiplication operations. The quantized RPReLU structure enables more efficient bit operations by constraining its slope to be integer powers of 2. Experimental results achieved 92.30%, 69.35%, and 66.89% on the CIFAR-10, CIFAR-100, and ImageNet datasets, respectively, which are competitive with the state-of-the-art. Ablation studies have verified the efficacy of the quantized RPReLU structure, leading to a 1.14% enhancement on the ImageNet compared to using a fixed slope RLeakyReLU. The proposed add&bit-operation-only BNN offers an innovative approach for hardware-friendly network architecture.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on ImageNet (val) (ACC)](<https://www.sota2.com/research/sota/image-classification-on-imagenet-val-acc.md>): Image Classification; ImageNet (val); Accuracy: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.03739>)

[PDF](<https://arxiv.org/pdf/2403.03739.pdf>)

[Implementation](<https://github.com/Ruichen0424/AB-BNN>)