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A Low-power convolutional neural network (CNN)-based face recognition system is proposed for the user authentication in smart devices. The system consists of two chips: an always-on CMOS image sensor (CIS)-based face detector (FD) and a low-power CNN processor. For always-on FD, analog-digital Hybrid Haar-like FD is proposed to improve the energy efficiency of FD by 39%. For lowpower CNN processing, the CNN processor with 1024 MAC units and 8192-bit-wide local distributed memory operates at near threshold voltage, 0.46 V with 5-MHz clock frequency. In addition, the separable filter approximation is adopted for the workload reduction of CNN, and transpose-read SRAM using 7T SRAM cell is proposed to reduce the activity factor of the data read operation. Implemented in 65-nm CMOS technology, the 3.30 × 3.36 mm 2 CIS chip and the 4 × 4 mm 2 CNN processor consume 0.62 mW to evaluate one face at 1 fps and achieved 97% accuracy in LFW dataset.
Bong et al. (Wed,) studied this question.