查看文章

An Event-driven visual data Processing Unit (EPU) exploits temporal redundancy in stationary camera video streams to localize motion-based Regions-of-Interest (RoI), saving compute FLOPs and memory bandwidth (BW) for Deep Learning (DL) based object detection. The proposed EPU supports FHD frames at 70fps and can be time-multiplexed across multiple video streams. The EPU pipeline consists of event detection, event clustering, event cluster dilation and RoI extraction and occupies 0.34mm ² in 10nm CMOS. Frame-based, inter- and intra-frame event-driven power management schemes minimize normalized energy/pixel to 0.05pJ at 0.65V. RoI filtering with an EPU frontend improves the end-to-end (E2E) energy-efficiency of a deep-learning (DL) based vision pipeline by 5X, while improving its throughput by 4.3X.