MACSen: A Processing-In-Sensor Architecture Integrating MAC Operations Into Image Sensor for Ultra-Low-Power BNN-Based Intelligent Visual Perception

Current BNN-based visual system wastes lots of energy in data conversion and movement, hindering its deployment on battery-powered devices. This brief proposes MACSen, an ultra-low-power processing-in-sensor (PIS) architecture which integrates sensing with computing and directly outputs the computation results. The multiply-and-accumulation (MAC) operation in BNN is fused with the Correlated Double Sampling (CDS) procedure together to save data conversion power. A $4\times 4$ MACSen prototype of 180nm process was fabricated for demonstration, and it achieves the frame rate of 1000fps and the energy efficiency of 1.32TOP/s/W in computation mode. Furthermore, the system demonstration on MNIST dataset classification task shows that the hardware BNN implementation integrating MACSen incurs no accuracy degradation and gains 61% energy saving compared with state-of-the-art work.