Photonic neuromorphic information processing and reservoir computing

被引：78

作者：

Lugnan, A. ^{[1
]}

Katumba, A. ^{[1
]}

Laporte, F. ^{[1
]}

Freiberger, M. ^{[2
]}

Sackesyn, S. ^{[1
]}

Ma, C. ^{[1
]}

Gooskens, E. ^{[1
]}

Dambre, J. ^{[2
]}

Bienstman, P. ^{[1
]}

机构：

[1] Univ Ghent, Dept Informat Technol, IMEC, Technol Pk Zwijnaarde 126, B-9052 Ghent, Belgium

[2] Univ Ghent, IDLab, IMEC, Technol Pk Zwijnaarde 126, B-9052 Ghent, Belgium

来源：

APL PHOTONICS | 2020年 / 5卷 / 02期

基金：

欧盟地平线“2020”;

关键词：

TIMING-DEPENDENT PLASTICITY; OPTICAL FEEDBACK; PERFORMANCE; NETWORKS; IMPLEMENTATION; DYNAMICS;

D O I：

10.1063/1.5129762

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Photonic neuromorphic computing is attracting tremendous research interest now, catalyzed in no small part by the rise of deep learning in many applications. In this paper, we will review some of the exciting work that has been going in this area and then focus on one particular technology, namely, photonic reservoir computing.

引用

页数：14

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