All-optical spiking neurosynaptic networks with self-learning capabilities.

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ID: 100064
2019
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Abstract
Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses. Such hardware, when connected in networks or neuromorphic systems, processes information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system, capable of supervised and unsupervised learning. We exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain. Such photonic neurosynaptic networks promise access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data.
Reference Key
feldmann2019allopticalnature Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Feldmann, J;Youngblood, N;Wright, C D;Bhaskaran, H;Pernice, W H P;
Journal Nature
Year 2019
DOI
10.1038/s41586-019-1157-8
URL
Keywords
Internet of Things routing protocol loadng rpl low-power and lossy network

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