Optimization of XNOR Convolution for Binary Convolutional Neural Networks on GPU

2020-10-09
KAYA, METE CAN
İNCİ, ALPEREN
TEMİZEL, ALPTEKİN
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.
Citation Formats
M. C. KAYA, A. İNCİ, and A. TEMİZEL, “Optimization of XNOR Convolution for Binary Convolutional Neural Networks on GPU,” presented at the 6. Ulusal Yüksek Başarımlı Hesaplama Konferansı (BAŞARIM 2020), Ankara, Turkey, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73720.