Affiliation:
1. Yongjiang Laboratory
2. Nanjing University
3. Chinese Academy of Sciences
4. Nantong University
5. University of Glasgow
Abstract
Photonic neuromorphic computing has emerged as a promising approach to
building a low-latency and
energy-efficient non-von Neuman computing system. A photonic spiking
neural network (PSNN) exploits brain-like spatiotemporal processing to
realize high-performance
neuromorphic computing. However, the nonlinear computation of a PSNN
remains a significant challenge. Here, we propose and fabricate a
photonic spiking neuron chip based on an integrated Fabry–Perot laser
with a saturable absorber (FP-SA). The nonlinear neuron-like dynamics
including temporal integration, threshold and spike generation, a
refractory period, inhibitory behavior and cascadability are
experimentally demonstrated, which offers an indispensable fundamental
building block to construct the PSNN hardware. Furthermore, we propose
time-multiplexed temporal spike encoding to realize a functional PSNN
far beyond the hardware integration scale limit. PSNNs with
single/cascaded photonic spiking neurons are experimentally
demonstrated to realize hardware-algorithm collaborative computing,
showing the capability to perform classification tasks with a
supervised learning algorithm, which paves the way for a multilayer
PSNN that can handle complex tasks.
Funder
National Key Research and Development
Program of China
National Natural Science Foundation of
China
National Outstanding Youth Science Fund
Project of National Natural Science Foundation of
China
Fundamental Research Funds for the
Central Universities
Subject
Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Cited by
25 articles.
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