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.
@article{arxiv.2408.08320,
title = {Hardware-Algorithm Re-engineering of Retinal Circuit for Intelligent Object Motion Segmentation},
author = {Jason Sinaga and Victoria Clerico and Md Abdullah-Al Kaiser and Shay Snyder and Arya Lohia and Gregory Schwartz and Maryam Parsa and Akhilesh Jaiswal},
journal= {arXiv preprint arXiv:2408.08320},
year = {2024}
}