A 45.2- μ W Real-Time ASL Gesture Recognition System With Hierarchical Self-Similar Binary Neural Network for Smart Edge Devices

American Sign Language (ASL) has drawn growing attention due to its extensive application potential. This brief presents an ultralow-power real-time high-accuracy ASL recognition system (LRASL) for smart edge devices. It balances accuracy and power efficiency via a six-stage advanced hand region segmentation engine (AHRSE) and a hardware-sharing hierarchical self-similar binarized neural network engine (HSBNNE). The AHRSE compresses$640\times 480$RGB565 inputs into$32\times 32$binary gestures through six-stage processing, achieving a$4800\times $reduction in size, and boosting HSBNNE accuracy by 1.3%. Notably, similar gestures persist in binarized ASL gestures after extraction, increasing recognition difficulty and elevating hardware’s power consumption/resource demands. To address this, a novel miniaturized hierarchical self-similar binarized neural network (HSBNN) model with shared layers and dual self-similar expert branches is proposed. The HSBNNE, built on the HSBNN model, recognizes ASL gestures (similar and dissimilar ones) from coarse to fine, which achieves 95.8% recognition accuracy with only 3.96 MOPs and 2.86 KB parameters. The self-similar expert branches enable maximal reuse of hardware resources, reducing hardware overhead and lowering power consumption. Implemented in TSMC 28-nm CMOS, the proposed LRASL achieves a power consumption of only$45.2~\mu $W and a latency of 33.3 ms when operating at an 800-kHz clock frequency and 0.6-V supply voltage.