In this paper, we describe applying techniques for accelerating neural network algorithms on mobile embedded systems. MNIST handwriting artificial neural networks that identify numerical images are accelerated and compared using OpenCL, OpenMP, NEON, and other performance optimization techniques.
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. 2015R1A2A2A01008434) and Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-01696).
