  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
