Reconfigurable Retina-Inspired Looming Detection
Jason Sinaga, Shay Snyder, Md Abdullah-Al Kaiser, Dan Jinoy, Gregory Schwartz, Maryam Parsa, Akhilesh Jaiswal · 2025
Recent advances in retinal neuroscience inspired the development of hardware and software systems that leverage evolutionarily derived retinal computations for real-world computer vision applications. In this work, we propose a novel, reconfigurable CMOS circuit designed specifically for Looming Detection (LD), a key retinal computation associated with detecting rapidly approaching objects and potential threats. We analyze the circuit's performance using real-world data from controlled laboratory environments and hardware-aware algorithmic simulations, demonstrating its accuracy in identifying potential collisions and avoiding false-positives from mundane object movements. Furthermore, we evaluate the CMOS hardware characteristics using GlobalFoundries' 22nm FD-SOI technology, exhibiting a 0.36 pJ energy consumption per looming spike for a unit kernel size of 5 × 5 pixels. This work contributes a foundational approach to adaptive hardware-software co-design by integrating advances in retinal neuroscience with modern CMOS technology to address real-time LD with in-sensor computations.