Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent Object Motion Segmentation

TL;DR

This paper presents a hardware-algorithm co-design approach inspired by retinal circuits, implementing object motion sensitivity directly within image sensors for improved motion segmentation in dynamic environments.

Contribution

It introduces novel CMOS circuits for in-sensor OMS functionality with re-configurability, bridging biological insights and hardware design for real-time vision applications.

Findings

CMOS circuits successfully simulate OMS functionality

Re-configurable design adapts to dynamic environments

Cadence simulations validate circuit performance

Abstract

Recent advances in retinal neuroscience have fueled various hardware and algorithmic efforts to develop retina-inspired solutions for computer vision tasks. In this work, we focus on a fundamental visual feature within the mammalian retina, Object Motion Sensitivity (OMS). Using DVS data from EV-IMO dataset, we analyze the performance of an algorithmic implementation of OMS circuitry for motion segmentation in presence of ego-motion. This holistic analysis considers the underlying constraints arising from the hardware circuit implementation. We present novel CMOS circuits that implement OMS functionality inside image sensors, while providing run-time re-configurability for key algorithmic parameters. In-sensor technologies for dynamical environment adaptation are crucial for ensuring high system performance. Finally, we verify the functionality and re-configurability of the proposed CMOS circuit designs through Cadence simulations in 180nm technology. In summary, the presented work lays foundation for hardware-algorithm re-engineering of known biological circuits to suit application needs.