Retinomorphic hardware for in‐sensor computing

TLDR

The rapid growth of the Internet of Things and artificial intelligence demands advanced image perception, yet von Neumann architectures impose energy, latency, and cost burdens. This review surveys retinomorphic devices for in‑sensor computing, outlining strategies to overcome challenges and proposing a vision for neuromorphic architecture. The authors examine various photoelectric sensor mechanisms and discuss design strategies for retinomorphic in‑sensor computing. The review summarizes the potential applications of retinomorphic hardware. Image included.

Abstract

Abstract Rapid developments in the Internet of Things and Artificial Intelligence trigger higher requirements for image perception and learning of external environments through visual systems. However, limited by von Neumann's bottleneck, the physical separation of sense, memory, and processing units in a conventional personal computer‐based vision system tend to consume a significant amount of energy, time latency, and additional hardware costs. By integrating computational tasks of multiple functionalities into the sensors themselves, the emerging bio‐inspired neuromorphic visual systems provide an opportunity to overcome these limitations. With high speed, ultralow power and strong adaptability, it is highly desirable to develop a neuromorphic vision system that is based on highly precise in‐sensor computing devices, namely retinomorphic devices. We here present a timely review of retinomorphic devices for visual in‐sensor computing. We begin with several types of physical mechanisms of photoelectric sensors that can be constructed for artificial vision. The potential applications of retinomorphic hardware are, thereafter, thoroughly summarized. We also highlight the possible strategies to existing challenges and give a brief perspective of retinomorphic architecture for in‐sensor computing. image