A 15.2 TOPS/W CNN Accelerator with Similar Feature Skipping for Face Recognition in Mobile Devices
Sangyeob Kim, Juhyoung Lee, Sanghoon Kang, Jinsu Lee, Hoi‐Jun Yoo · 2019
A low-power face recognition processor with similar feature skipping (SFS) and the tile-based clustering algorithm is proposed for high energy efficiency in mobile devices. For higher energy efficiency face recognition (FR) processor, this paper proposes two key features: 1) Tile-based clustering enables to reduce computation overhead of clustering. 2) SFS binary convolution core is proposed to increase energy efficiency, resulting in 15.2 TOPS/W energy efficiency. Implemented with 65 nm CMOS technology, the 6 mm2FR processor achieves 0.26mW power consumption at 1 frames-per-second (fps) always-on face recognition in mobile devices.