光子学
人工神经网络
计算机科学
可扩展性
油藏计算
硅光子学
炸薯条
电子工程
循环神经网络
人工智能
电信
物理
光电子学
工程类
数据库
作者
Satoshi Sunada,Atsushi Uchida
出处
期刊:Optica
[The Optical Society]
日期:2021-11-02
卷期号:8 (11): 1388-1388
被引量:25
标识
DOI:10.1364/optica.434918
摘要
Photonic neural networks have significant potential for high-speed neural processing with low latency and ultralow energy consumption. However, the on-chip implementation of a large-scale neural network is still challenging owing to its low scalability. Herein, we propose the concept of a photonic neural field and implement it experimentally on a silicon chip to realize highly scalable neuro-inspired computing. In contrast to existing photonic neural networks, the photonic neural field is a spatially continuous field that nonlinearly responds to optical inputs, and its high spatial degrees of freedom allow for large-scale and high-density neural processing on a millimeter-scale chip. In this study, we use the on-chip photonic neural field as a reservoir of information and demonstrate a high-speed chaotic time-series prediction with low errors using a training approach similar to reservoir computing. We show that the photonic neural field is potentially capable of executing more than one peta multiply–accumulate operations per second for a single input wavelength on a footprint as small as a few square millimeters. The operation of the neural field is energy efficient due to a passive scattering process, for which the required power comes only from the optical input. We also show that in addition to processing, the photonic neural field can be used for rapidly sensing the temporal variation of an optical phase, facilitated by its high sensitivity to optical inputs. The merging of optical processing with optical sensing paves the way for an end-to-end data-driven optical sensing scheme.
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