Optimization of XNOR Convolution for Binary Convolutional Neural Networks on GPU

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-constraine...

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Bibliographic Details
Published in:arXiv.org 2020-07
Main Authors: Mete Can Kaya, Alperen İnci, Temizel, Alptekin
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer vision
Convolution
Electronic devices
Embedded systems
Inference
Optimization
Online Access:Get full text
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Summary: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
ISSN:2331-8422