Paper ID: 2408.08320

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

Jason Sinaga (1), Victoria Clerico (2, 3), Md Abdullah-Al Kaiser (1), Shay Snyder (2), Arya Lohia (2), Gregory Schwartz (4), Maryam Parsa (2), Akhilesh Jaiswal (1) (University of Wisconsin - Madison (1), George Mason University (2), Universidad Politécnica de Madrid (3), Northwestern University (4))

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.

Submitted: Jul 31, 2024