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A Photonic Recurrent Neuron for Time-Series Classification

Neuromorphic photonics has turned into a key research area for enabling neuromorphic computing at much higher data-rates compared to their electronic counterparts, improving significantly the (multiply-and-accumulate) MAC/sec. At the same time, time-series classification problems comprise a large class of artificial intelligence (AI) applications where speed and latency can have a decisive role in their hardware deployment roadmap, highlighting the need for ultra-fast hardware implementations of simplified recurrent neural networks (RNN) that can be extended in more advanced long-short-term-memory (LSTM) and gated recurrent unit (GRU) machines. Herein, we experimentally demonstrate a novel photonic recurrent neuron (PRN) to classify successfully a time-series vector with 100-psec optical pulses and up to 10 Gb/s data speeds, reporting on the fastest all-optical real-time classifier. Experimental classification of 3-bit optical binary data streams is presented, revealing an average accuracy of >91% and confirming the potential of PRNs to boost speed and latency performance in time-series AI applications.

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Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.39 No.5 March 1 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1340 - 1347

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

NEUROMORPHIC COMPUTING
RECURRENT NEURAL NETWORKS
NEUROMORPHIC PHOTONICS
OPTICAL NEURAL NETWORKS
PROGRAMABLE PHOTONICS

Info Detail Spesifik

DOI: 10.1109/JLT.2020.3038890

Pernyataan Tanggungjawab

George Mourgias-Alexandris, Nikolaos Passalis, George Dabos, Angelina Totovic, Anastasios Tefas, Nikos Pleros

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