Optimization of XNOR Convolution for Binary Convolutional Neural Networks on GPU

Date

2020-10-09

Author

Kaya, Mete Can
İnci, Alperen
Temizel, Alptekin

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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 resourceconstrained 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× with a kernel size of 3×3. The implementation is publicly available at https://github.com/metcan/ Binary-Convolutional-Neural-Network-Inference-on-GPU.

Subject Keywords

Computer Vision and Pattern Recognition (cs.CV), Distributed, Parallel, and Cluster Computing (cs.DC)

URI

https://hdl.handle.net/11511/73720
https://arxiv.org/pdf/2007.14178.pdf

Conference Name

6. Ulusal Yüksek Başarımlı Hesaplama Konferansı (BAŞARIM 2020)

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

M. C. Kaya, A. İnci, and A. Temizel, “Optimization of XNOR Convolution for Binary Convolutional Neural Networks on GPU,” presented at the 6. Ulusal Yüksek Başarımlı Hesaplama Konferansı (BAŞARIM 2020), Ankara, Türkiye, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73720.