大规模光子脉冲硬件系统的实现

Ria Talukder, X. Porte, D. Brunner
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引用次数: 0

摘要

神经网络的光子硬件集成具有并行性、高速数据处理和潜在的低能耗等优点。在人工神经网络(ANN)中,神经元分为静态、单值和连续值。相反,生物神经元的信息传递和计算是通过峰发生的,其中峰的时间和速率起着重要的作用。因此,脉冲神经网络(snn)在生物学上更具相关性,并且在硬件友好性和能源效率方面具有额外的好处。考虑到这些优点,我们设计了一种基于光子循环尖峰神经网络(SNN)的光子库计算机(RC),即一种液态机。这是一个可扩展的概念验证实验,包含超过30,000个神经元。该系统为下一代光子系统的仿生学习提供了一个很好的实验平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Realisation of large-scale photonic spiking hardware system
An efficient photonic hardware integration of neural networks can benefit us from the inherent properties of parallelism, high-speed data processing and potentially low energy consumption. In artificial neural networks (ANN), neurons are classified as static, single and continuous-valued. On contrary, information transmission and computation in biological neurons occur through spikes, where spike time and rate play a significant role. Spiking neural networks (SNNs) are thereby more biologically relevant along with additional benefits in terms of hardware friendliness and energy-efficiency. Considering all these advantages, we designed a photonic reservoir computer (RC) based on photonic recurrent spiking neural networks (SNN) i.e. a liquid state machine. It is a scalable proof-of-concept experiment, comprising more than 30,000 neurons. This system presents an excellent testbed for demonstrating next generation bio-inspired learning in photonic systems.
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