Optimization of XNOR Convolution for Binary Convolutional Neural Networks on GPU

TL;DR

This paper presents an optimized GPU implementation of XNOR convolution for binary neural networks, significantly accelerating inference and enabling real-time deployment on embedded devices.

Contribution

It introduces a specialized GPU optimization for XNOR convolution in binary neural networks, improving inference speed and practicality for embedded systems.

Findings

GPU implementation achieves up to 42.61x speed-up

Effective for 3x3 kernel sizes

Enables real-time inference on embedded devices

Abstract

Binary convolutional networks have lower computational load and lower memory foot-print compared to their full-precision counterparts. So, they are a feasible alternative for the deployment of computer vision applications on limited capacity embedded devices. Once trained on less resource-constrained computational environments, they can be deployed for real-time inference on such devices. In this study, we propose an implementation of binary convolutional network inference on GPU by focusing on optimization of XNOR convolution. Experimental results show that using GPU can provide a speed-up of up to $42.61\times$ with a kernel size of $3\times3$. The implementation is publicly available at https://github.com/metcan/Binary-Convolutional-Neural-Network-Inference-on-GPU