论文标题
在GPU上的二元卷积神经网络的XNOR卷积优化
Optimization of XNOR Convolution for Binary Convolutional Neural Networks on GPU
论文作者
论文摘要
与其全精确的对应物相比，二进制卷积网络具有较低的计算负载和较低的内存足迹。因此，它们是在有限容量嵌入式设备上部署计算机视觉应用程序的可行替代方法。一旦在资源受限的计算环境中接受培训后，可以在此类设备上进行实时推断。在这项研究中，我们提出了通过专注于Xnor卷积的优化来实施GPU的二元卷积网络推断。实验结果表明，使用GPU可以提供高达$ 42.61 \ times $的加速$ 3 $ 3 $。该实现可在https://github.com/metcan/binary-convolutional-neural-network-interwork-inference-on-gpu上公开获取
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
