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A Low-Power Convolutional Neural Network Face Recognition Processor and a CIS Integrated With Always-on Face Detector
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 Hybri...
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Published in: | IEEE journal of solid-state circuits 2018-01, Vol.53 (1), p.115-123 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | 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. |
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ISSN: | 0018-9200 1558-173X |
DOI: | 10.1109/JSSC.2017.2767705 |