A Power-Efficient CNN Accelerator With Similar Feature Skipping for Face Recognition in Mobile Devices

Abstract

A low power face recognition (FR) convolutional neural network (CNN) processor is proposed with high power efficiency to achieve always-on FR in mobile devices. Three key features enable a power-efficient FR CNN. First, tile-based clustering (THC) is proposed for reducing the computation overhead of hierarchical clustering. It generates an average of 37.2% duplicated input features in the entire network. Second, a low latency tile-based hierarchical clustering core is proposed. It supports an approximated clustering method that removes distance updates and increases pipeline utilization by up to 98.6%. Finally, a similar feature skipping binary convolution core with separated accumulation cores is proposed to increase power efficiency through skipping redundant MAC operation between duplicated input features and weights. It can skip 38.9% of CNN operations and reduce 35.8% of the total computation considering both CNN and THC. The proposed features enable the proposed processor to operate FR CNN with 17.3 TOPS/W power efficiency, which is $1.3\times $ higher compared to the previous state-of-the-art FR CNN processor. Implemented in 65nm CMOS technology, the 6mm2 FR CNN processor shows 0.5mW power consumption at 1 frames-per-second always-on face recognition.