仿生视网膜设计实现微秒级低功耗光流感知
Optical Flow Sensor: A Direction-Selective Bionic Retina Design
- 像素级并行计算，结合事件对比与时间差测量
- 功耗降低303倍，输出数据量减少3.3倍
- 适合高速低功耗视觉传感，如机器人导航
光流表征视觉场中的运动，是生物与人工视觉系统中运动感知和追踪的基础。生物视网膜通过局部ON/OFF通路和并行处理高效提取运动信息，而传统帧基光流依赖密集采样和全局计算，导致高延迟和高功耗。为此，我们提出一种像素级光流传感器（OFS）集成电路，融合动态视觉传感器（DVS）的ON/OFF事件比较与时差测量，实现片上完全并行的光流计算。一种面向光流的地址事件表示（OF-AER）接口支持低功耗、高吞吐读出。基于CMOS的OFS进一步提出基于光忆阻器的OFS，以降低功耗与面积开销。实验表明，该OFS相比FPGA加速的DVS系统功耗降低303倍，保持微秒级延迟；直接输出光流矢量，使输出数据量减少约3.3倍，展现出在超高速、低功耗视觉传感中的强大潜力。
原文摘要 · Abstract (English)
Optical flow characterizes motion in the visual field and is fundamental to motion perception and tracking in biological and artificial vision systems. Biological retinas extract motion efficiently through local ON/OFF pathways and parallel processing, while conventional frame-based optical flow relies on dense sampling and global computation, resulting in high latency and power consumption. To overcome these limitations, we present a pixel-level Optical Flow Sensor (OFS) integrated circuit. The design combines Dynamic Vision Sensor (DVS) ON/OFF event comparison with time-difference measurement to enable fully parallel optical flow computation on-chip. An optical-flow-specific Address-Event Representation (OF-AER) interface supports low-power, high-throughput readout. \rev{Based on the CMOS-based OFS, we further propose optical memristor-based OFS to reduce sensor power consumption and area overhead.} Experimental results show that the proposed OFS achieves a 303$\times$ reduction in power consumption compared with FPGA-accelerated DVS systems while maintaining microsecond-level latency. Moreover, by directly outputting optical flow vectors, the OFS reduces output data size by approximately 3.3$\times$, demonstrating strong potential for ultra-high-speed, low-power vision sensing applications.
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