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 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.
Highlights
Neuromorphic computing has intrinsically enhanced computing power in the last decade
The electronic hardware implementation of spiking neural network (SNN) suffers from energy efficiency and physical dimensions, as well as the fundamental tradeoff between bandwidth and interconnectivity
The photonic platform has become a promising candidate for neuromorphic hardware implementation due to the advantages of ultrahigh speed, high efficiency, and extremely high bandwidth
Summary
Neuromorphic computing has intrinsically enhanced computing power in the last decade. The semiconductor optical amplifiers (SOA) and the vertical-cavity SOA (VCSOA) were numerically and experimentally demonstrated to perform the spike timing dependent plasticity (STDP) function [20,21], which is a biologically observed phenomenon in synapses that related to the learning mechanism [22]. Neurons based on micro-cavity possess an internal mechanism for generating optical pulses due to the nonlinear effects in silicon. Such neurons are CMOS-compatible, promising for large-scale integration, and have very low loss at telecom wavelengths.
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