Neuromorphic Vision Hybrid RRAM-CMOS Architecture

TLDR

Bioinspired image sensors that emulate vertebrate retina functionality open new opportunities for vision systems and processing. The study synthesizes the photocurrent signal flow from a 128×128 active pixel sensor to a 16×16 spike‑output array in scalable 180‑nm CMOS, modeling retinal ganglion cell output. The architecture uses layers of memristive networks to emulate horizontal and amacrine cells, averaging and converging signals, and forwards the spike train to a visual cortex for interpretation. The resulting images match biological benchmarks within 6% error and display lateral inhibition, asynchronous adaptation, and low‑dynamic‑range integration enabling high‑dynamic‑range perception.

Abstract

The development of a bioinspired image sensor, which can match the functionality of the vertebrate retina, has provided new opportunities for vision systems and processing through the realization of new architectures. Research in both retinal cellular systems and nanodriven memristive technology has made a challenging arena more accessible to emulate features of the retina that are closer to biological systems. This paper synthesizes the signal flow path of photocurrent throughout a retina in a scalable 180-nm CMOS technology, which initiates at a 128 × 128 active pixel image sensor, and converges to a 16 × 16 array, where each node emits a spike train synonymous to the function of the retinal ganglionic output cell. This signal can be sent to the visual cortex for image interpretation as part of an artificial vision system. Layers of memristive networks are used to emulate the functions of horizontal and amacrine cells in the retina, which average and converge signals. The resulting image matches biologically verified results within an error margin of 6% and exhibits the following features of the retina: lateral inhibition, asynchronous adaptation, and a low-dynamic-range integration active pixel sensor to perceive a high-dynamic-range scene.