Spectral Convolutional Neural Network Chip for In-sensor Edge Computing of Incoherent Natural Light

Abstract

Convolutional neural networks (CNNs) are representative models of artificial neural networks (ANNs). However, the considerable power consumption and limited computing speed of electrical computing platforms restrict further CNN development on edge devices. Optical neural networks are considered next-generation physical implementations of ANNs, but their capabilities are limited by on-chip integration scale and requirement for coherent light sources. This study proposes a spectral convolutional neural network (SCNN) of incoherent natural light by an optical convolutional layer (OCL) and a reconfigurable electrical backend. The OCL is implemented by integrating very large-scale, pixel-aligned spectral filters on a CMOS image sensor on a 12-inch wafer, facilitating highly parallel spectral vector-inner products of incident light. It accepts broadband incoherent natural light containing two spatial and one spectral dimension directly as input with the function of matter meta-imaging. This unique optoelectronic framework empowers in-sensor optical analog computing at extremely high energy efficiency because the OCL is driven by the energy of the information carrier, i. e. natural light. To the best of our knowledge, this is the first integrated optical computing utilizing natural light. We employ the same SCNN chip for completely different real-world complex tasks,and achieve accuracies of over 96% for pathological diagnosis and almost 100% for face anti-spoofing at video rates. The SCNN framework has an unprecedented new function of substance identification, provides a feasible optoelectronic and integrated optical CNN implementation for edge devices or cellphones, providing them with practical and powerful edge computing abilities and facilitating diverse applications, such as intelligent robotics, industrial automation, medical diagnosis, and remote sensing.