当前位置: X-MOL 学术arXiv.cs.ET › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing
arXiv - CS - Emerging Technologies Pub Date : 2021-05-22 , DOI: arxiv-2105.10672
Satoshi Sunada, Atsushi Uchida

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 discuss 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. 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.

中文翻译:

硅芯片上的光子神经场:大规模,高速神经启发的计算和传感

光子神经网络具有低延迟和超低能耗的高速神经处理的巨大潜力。但是,大规模神经网络的片上实现由于其可扩展性低而仍然具有挑战性。本文中,我们提出了光子神经场的概念,并在硅芯片上实验性地实现了它,以实现高度可扩展的神经启发式计算。与现有的光子神经网络相反,光子神经场是一个空间连续的场,它非线性地响应光学输入,其高空间自由度允许在毫米级芯片上进行大规模和高密度的神经处理。在这项研究中,我们使用片上光子神经场作为信息存储库,并使用类似于存储库计算的训练方法演示了具有低误差的高速混沌时间序列预测。我们讨论光子神经场潜在地能够在小至几平方毫米的足迹上针对单个输入波长每秒执行一次以上的peta乘加运算。除了处理之外,由于其对光输入的高灵敏度,光子神经场还可用于快速检测光相位的时间变化。光学处理与光学传感的融合为端到端数据驱动的光学传感方案铺平了道路。我们讨论光子神经场潜在地能够在小至几平方毫米的足迹上针对单个输入波长每秒执行一次以上的peta乘加运算。除了处理之外,由于其对光输入的高灵敏度,光子神经场还可用于快速检测光相位的时间变化。光学处理与光学传感的融合为端到端数据驱动的光学传感方案铺平了道路。我们讨论光子神经场潜在地能够在小至几平方毫米的足迹上针对单个输入波长每秒执行一次以上的peta乘加运算。除了处理之外,由于其对光输入的高灵敏度,光子神经场还可用于快速检测光相位的时间变化。光学处理与光学传感的融合为端到端数据驱动的光学传感方案铺平了道路。对光输入的高灵敏度使其变得容易。光学处理与光学传感的融合为端到端数据驱动的光学传感方案铺平了道路。对光输入的高灵敏度使其变得容易。光学处理与光学传感的融合为端到端数据驱动的光学传感方案铺平了道路。
更新日期:2021-05-25
down
wechat
bug