Fast integer approximations in convolutional neural networks using layer-by-layer training
Dmitry Ilin, Elena E. Limonova, Vladimir V. Arlazarov, Dmitry Nikolaev · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
This paper explores method of layer-by-layer training for neural networks to train neural network, that use approximate calculations and/or low precision data types. Proposed method allows to improve recognition accuracy using standard training algorithms and tools. At the same time, it allows to speed up neural network calculations using fast-processed approximate calculations and compact data types. We consider 8-bit fixed-point arithmetic as the example of such approximation for image recognition problems. In the end, we show significant accuracy increase for considered approximation along with processing speedup.