Optical Flow Sensor: A Direction-Selective Bionic Retina Design

2026-07-30

AI Summary

This work addresses the inherent trade-off between speed and power efficiency in conventional optical flow methods, which rely on dense frame sampling and global computation. The authors present the first pixel-level optical flow sensor (OFS) chip that directly computes direction-selective optical flow within each pixel, integrating dynamic vision sensing, event-driven computation, and temporal difference measurement. A novel OF-AER data interface is introduced to enhance communication efficiency. The design explores both CMOS and memristor-based architectures, substantially reducing area and power overhead. Compared to an FPGA-accelerated dynamic vision sensor (DVS) system, the proposed OFS achieves a 303× reduction in power consumption while maintaining microsecond-level latency and reducing output data volume by 3.3×.

Abstract

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.

Problem

• optical flow
• high latency
• power consumption
• frame-based vision
• motion perception

Innovation

• Optical Flow Sensor
• Dynamic Vision Sensor
• Event-Based Vision
• OF-AER
• Memristor-Based Sensor