Advances in silicon-based in-sensor computing for neuromorphic vision sensors

Abstract

With the development of the Internet of Things (IoT), intelligent security systems, assisted driving and artificial intelligence, traditional machine vision sensors no longer meet the needs of low power consumption, high sampling rate, high dynamic range and low data redundancy, therefore, the emergence of new vision sensors is urgently needed. Drawing on the information extraction and encoding methods of biological retinas, in-sensor computing photoelectric detection systems have been developed. It has a more efficient sampling model and data transfer mode than frame-based and separated sensory-computing systems. This paper provides an overview of several major silicon-based in-sensor computing neuromorphic vision sensors. Using bio-vision mechanisms as an entry point, the architecture and working principles of these neuromorphic optoelectronic sensors are focused on, their application scenarios, advantages and limitations are compared, and the challenges and possible directions for development are analysed.