集成光子神经形态计算:机遇与挑战

Nikolaos Farmakidis, Bowei Dong, Harish Bhaskaran
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引用次数: 0

摘要

几十年来,用光子代替电子来处理信息一直是一个令人兴奋的技术前景。由于光子集成电路日趋成熟,以及迫切需要更快的处理速度来应对人工智能产生的数据,光计算正重新获得人们的热情。在神经形态光子学中,光的玻色性被用于高速、密集复用的线性运算,而生物神经元的卓越计算模式则被用于加速计算。在此,我们将概述集成突触光学器件和片上光子神经网络的最新进展,重点关注光电转换在架构中的位置。我们介绍了与电光转换相关的挑战、光学非线性的实现、放大和时域处理,并确定了前景广阔的新兴光子神经形态硬件。神经形态光子学是一种新兴计算平台,可满足现代社会日益增长的计算需求。我们回顾了集成神经形态光子学的进展,讨论了与电光转换、非线性实现、放大和时域处理相关的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Integrated photonic neuromorphic computing: opportunities and challenges

Integrated photonic neuromorphic computing: opportunities and challenges
Using photons in lieu of electrons to process information has been an exciting technological prospect for decades. Optical computing is gaining renewed enthusiasm, owing to the accumulated maturity of photonic integrated circuits and the pressing need for faster processing to cope with data generated by artificial intelligence. In neuromorphic photonics, the bosonic nature of light is exploited for high-speed, densely multiplexed linear operations, whereas the superior computing modalities of biological neurons are imitated to accelerate computations. Here, we provide an overview of recent advances in integrated synaptic optical devices and on-chip photonic neural networks focusing on the location in the architecture at which the optical to electrical conversion takes place. We present challenges associated with electro-optical conversions, implementations of optical nonlinearity, amplification and processing in the time domain, and we identify promising emerging photonic neuromorphic hardware. Neuromorphic photonics is an emerging computing platform that addresses the growing computational demands of modern society. We review advances in integrated neuromorphic photonics and discuss challenges associated with electro-optical conversions, implementations of nonlinearity, amplification and processing in the time domain.
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