Year: 2020
Type: preprint
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
Source: arXiv (Cornell University)
Authors: Kaya, Mete Can, İnci, Alperen, Temizel, Alptekin
Institution: Middle East Technical University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3045877795