Abstract
The ongoing growth of use-cases for artificial neural networks (ANNs) fuels the search for new, tailor-made ANN-optimized hardware. Neuromorphic (brain-like) computers are among the proposed highly promising solutions, with optical neuromorphic realizations recently receiving increasing research interest. Among these, photonic neuronal models based on vertical cavity surface emitting lasers (VCSELs) stand out due to their favourable properties, fast operation and mature technology. In this work, we experimentally demonstrate different strategies to encode information into ultrafast spiking events in a VCSEL-neuron. We evaluate how the strength of the input perturbations (stimuli) influences the spike activation time, allowing for spike latency input coding. Based on a study of refractory behaviour in the system, we demonstrate the capability of the VCSEL-neuron to perform reliable binary-to-spike information coding with spiking rates surpassing 1 GHz. We also report experimentally on neuro-inspired spike firing rate-coding with a VCSEL-neuron, where the strength of the input perturbation (stimulus) is continuously encoded into the spiking frequency (spike firing rate). With the prospects of neuromorphic photonic systems constantly growing, we believe the reported functionalities with the ultrafast spiking VCSEL-neurons provide a reliable, multifaceted approach for interfacing photonic neuromorphic platforms with existing computation and communication systems.
Highlights
The advent of artificial neural networks (ANNs) marked a significant milestone in computer science, allowing computers to perform new complex tasks that typically require human reasoning, such as object recognition in images [1, 2] or natural language processing [3]
Upper time-traces denote the intensity of the modulated signal from the tunable laser source (TL) injected into the vertical cavity surface emitting lasers (VCSELs)-neuron, whilst lower time-traces correspond to the response of the VCSEL
We demonstrate experimentally the information encoding capabilities of a spiking photonic neuron running at ultrafast rates, based on an off-the-shelf VCSEL operating at telecom wavelengths
Summary
The advent of artificial neural networks (ANNs) marked a significant milestone in computer science, allowing computers to perform new complex tasks that typically require human reasoning, such as object recognition in images [1, 2] or natural language processing [3]. Conventional computers based on the von-Neumann architecture are not very well suited for ANNs due to their serial instruction processing and distinct memory and logic units These facts, paired with the physical limits on further transistor size minimization (Dennard’s scaling law [7]) and plateauing of Moore’s law observed in recent years [8] have fuelled the search for new, ‘beyond von-Neumann’ computational architectures. The field of neuromorphic (brain-like) engineering aims at answering those needs by mimicking the functionality of biological brains and nervous systems in large-scale, densely interconnected networks of computing primitives (typically analog circuits or digital processors). These systems are characterised by propagating information using spikes: an asynchronous, sparse information encoding scheme observed in neurons that combines properties of both digital and analog signals. While digital many-core systems (such as SpiNNaker) offer high degree of tunability for each node, making them suitable for simulations of neuronal networks in brains [17], the capability of analog systems to directly exhibit the dynamical behaviour similar to that observed in neurons, without the need to perform resource-intensive numerical simulation of it [18], further raises the prospects of these systems for high-speed, energy-efficient neuro-inspired computation
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