Senputing: An Ultra-Low-Power Always-On Vision Perception Chip Featuring the Deep Fusion of Sensing and Computing
Xu Han, Ningchao Lin, Li Luo, Qi Wei, Runsheng Wang, Cheng Zhuo, Xunzhao Yin, Fei Qiao, Huazhong Yang
Abstract
Always-on intelligent visual perception applications are widely deployed in edges in the AIoT era. In order to eliminate power costs of data conversion and transmission, this paper proposes Senputing, an ultra-low-power processing-in-sensor chip that completely fuses sensing and computing together for a BNN-based hierarchical processing system. This chip could operate in two modes. In computation mode, photocurrents are directly utilized for computing without being converted into voltages, and the computation results of 1-st BNN layer are directly sent out to subsequent BNN processors for an always-on coarse classification, eliminating conversion power and storage cost of raw images. Once an interested objected is detected, this chip switches to sensor mode and sends raw images to potential full-precision processors or cloud servers for fine-grained recognition or segmentation. A <inline-formula> <tex-math notation="LaTeX">$32\times 32$ </tex-math></inline-formula> prototype is fabricated with 180nm CMOS process. It accomplishes MNIST dataset classification task with the accuracy of 93.76% and the power consumption of 147nW at 156fps, achieving <inline-formula> <tex-math notation="LaTeX">$13.1\times $ </tex-math></inline-formula> energy efficiency compared with state-of-the-art work.