A Bio-Inspired Spiking Vision Chip Based on SPAD Imaging and Direct Spike Computing for Versatile Edge Vision

Edge vision devices face real 3-D scenes with lighting fluctuations. They require a vision chip capable of acquiring 2-D/3-D vision with a high dynamic range and performing real-time intelligent in situ processing, while maintaining low end-to-end latency. Aimed at this versatile edge vision, this article presents a bio-inspired full-spiking vision chip that consists of a single-photon-avalanche-diode (SPAD) image sensor and a spiking vision processor monolithically. To decrease latency while realizing versatile functions, we build a bio-inspired full spiking vision on chip by directly coupling spike-like SPAD imaging with a neuroinspired spike-computing paradigm. By modeling this spiking visual flow, we theoretically demonstrate direct coupling and derive a spike preprocessing method for dim-vision enhancement. The SPAD image sensor equipped with a configurable exposure gating scheme can realize three modes: 2-D, 3-D depth, and dim-vision imaging. The spiking vision processor contains a parallel processing element (PE) array and can be reconfigured as a preprocessor or a spiking neural network (SNN) processor. A prototype chip integrated with a
$128\ttimes 128$
SPAD image sensor with a reconfigurable spiking vision processor (256 PEs) was implemented in a 0.18-
$\mu$
m CMOS technology. The chip experimentally demonstrates three imaging modes, 99.30% classification accuracy on the MNIST dataset with 2-D vision, only 3.9% accuracy loss under 0.02 lux with dim-vision, 1.68 cm obstacle detection error with 3-D depth vision, and a 3.58
$\mu$
s latency for light adaptation. The chip realizes a 100 dB dynamic range, 100 000 spiking maps per second (SMps), and 81.92 GSOPS at an 80-MHz clock.