Photonic Enabled Neural Network: Key Components, Heterogeneous Architecture and Intelligent Applications

A special issue of Photonics (ISSN 2304-6732).

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 3715

Special Issue Editors


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Guest Editor
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Room 5-200, SEIEE Buildings, 800 Dongchuan Rd., Shanghai 200240, China
Interests: Intelligent Microwave Lightwave Integration (iMLic)

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Guest Editor
School of Science, Beijing Jiaotong University, Beijing 100044, China
Interests: all-optical signal processing; optical engineering; fiber optics; guided wave optics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: short reach optical interconnects and networking; ultra-high density and programmable optical chip; optical neural network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of novel computing hardware and architecture is of great strategic and economic significance to the leap-forward progress of artificial intelligence technology in the post-Moore era, and photonic enabled neural networks have become the disruptive path for future computing architecture due to their huge advantages in terms of processing speed and power consumption. In recent years, various integrated photonic neural networks, based on MZIs or microring resonators, have been proposed to accelerate the matrix operations. In addition, photonic computing architecture including convolutional neural networks, recurrent neural networks, and programmable reservoir computing systems are riding the wave of implementing large-scale intelligent applications such as vision, voice, and natural language classification. The purpose of this Special Issue is to highlight the progress in photonic-enabled neural networks, including key components, heterogeneous architectures, and intelligent applications. We believe that photonic involvement will foster new technologies for disruptive computing devices and architecture.

Prof. Dr. Weiwen Zou
Prof. Dr. Zhi Wang
Dr. Wenjia Zhang
Guest Editors


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Keywords

  • optical neural network
  • optical convolutional neural network
  • optical recurrent neural network
  • programmable photonics
  • optical unitary conversion

Published Papers (1 paper)

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Research

14 pages, 4238 KiB  
Communication
An All-MRR-Based Photonic Spiking Neural Network for Spike Sequence Learning
by Yanan Han, Shuiying Xiang, Yuna Zhang, Shuang Gao, Aijun Wen and Yue Hao
Photonics 2022, 9(2), 120; https://doi.org/10.3390/photonics9020120 - 20 Feb 2022
Cited by 6 | Viewed by 3209
Abstract
Photonic spiking neural networks (SNN) have the advantages of high power efficiency, high bandwidth and low delay, but limitations are encountered in large-scale integration. The silicon photonics platform is a promising candidate for realizing large-scale photonic SNN because it is compatible with the [...] Read more.
Photonic spiking neural networks (SNN) have the advantages of high power efficiency, high bandwidth and low delay, but limitations are encountered in large-scale integration. The silicon photonics platform is a promising candidate for realizing large-scale photonic SNN because it is compatible with the current mature CMOS platforms. Here, we present an architecture of photonic SNN which consists of photonic neuron, photonic spike timing dependent plasticity (STDP) and weight configuration that are all based on silicon micro-ring resonators (MRRs), via taking advantage of the nonlinear effects in silicon. The photonic spiking neuron based on the add-drop MRR is proposed, and a system-level computational model of all-MRR-based photonic SNN is presented. The proposed architecture could exploit the properties of small area, high integration and flexible structure of MRR, but also faces challenges caused by the high sensitivity of MRR. The spike sequence learning problem is addressed based on the proposed all-MRR-based photonic SNN architecture via adopting supervised training algorithms. We show the importance of algorithms when hardware devices are limited. Full article
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