5.9 A 0.8V Multimode Vision Sensor for Motion and Saliency Detection with Ping-Pong PWM Pixel
Tzu-Hsiang Hsu, Yen-Kai Chen, Jun-Shen Wu, Wen-Chien Ting, Cheng-Te Wang, Chen-Fu Yeh, Syuan-Hao Sie, Yiren Chen, Ren-Shuo Liu, Chung‐Chuan Lo, Kea‐Tiong Tang, Meng‐Fan Chang, Chih-Cheng Hsieh
Abstract
Energy-efficient always-on motion-detection (MD) sensors are in high demand and are widely used in machine vision applications. To achieve real-time and continuous motion monitoring, high-speed low-power temporal difference imagers with corresponding processing architectures are widely investigated [1-6]. Event-based dynamic vision sensors (DVS) have been reported to reduce the redundant data and power through the asynchronous timestamped event-address readout [1], [2]. However, DVS needs special data processing to collect enough events for information extraction, and suffers from noise and dynamic effects, which limits the advantages of low-latency pixel event reporting. Furthermore, low sensitivity (no integration) and lack of static information are also drawbacks of DVS. Frame-based MD rolling-shutter sensors [3], [4] were reported to reduce the data bandwidth and power by sub-sampling operation with the tradeoff of low resolution and motion blur. Global-shutter MD sensors were reported [5], [6] using in-pixel analog memory for reference image storage. However, such sensors require a special process technology for low off-state current device implementation. In a frame-based MD sensor, the required analog processing circuit and two successive frames for temporal difference operation comes at a cost in power, area, and speed. To address these drawbacks, we present a frame-based MD vision sensor featuring three operation modes: image-capture (IC), frame-difference (FD) with on/off event detection, and saliency-detection (SD). Using a low-voltage ping-pong PWM pixel and multi-mode operation, it achieves high-speed low-power full-resolution MD, consecutive event frame reporting, and image capture functions. Moreover, saliency detection by counting the block-level event number is also implemented for efficient optic flow extraction of the companion processing chip using simple neuromorphic circuits.