Improving neural network performance on SIMD architectures
Elena E. Limonova, Dmitry Ilin, Dmitry Nikolaev · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Neural network calculations for the image recognition problems can be very time consuming. In this paper we propose three methods of increasing neural network performance on SIMD architectures. The usage of SIMD extensions is a way to speed up neural network processing available for a number of modern CPUs. In our experiments, we use ARM NEON as SIMD architecture example. The first method deals with half float data type for matrix computations. The second method describes fixed-point data type for the same purpose. The third method considers vectorized activation functions implementation. For each method we set up a series of experiments for convolutional and fully connected networks designed for image recognition task.