Abstract
Driven by the increasing significance of artificial intelligence, the field of neuromorphic (brain-inspired) photonics is attracting increasing interest, promising new, high-speed, and energy-efficient computing hardware for key applications in information processing and computer vision. Widely available photonic devices, such as vertical-cavity surface emitting lasers (VCSELs), offer highly desirable properties for photonic implementations of neuromorphic systems, such as high-speed and low energy operation, neuron-like dynamical responses, and ease of integration into chip-scale systems. Here, we experimentally demonstrate encoding of digital image data into continuous, rate-coded, up to GHz-speed optical spike trains with a VCSEL-based photonic spiking neuron. Moreover, our solution makes use of off-the-shelf fiber-optic components with operation at telecom wavelengths, therefore making the system compatible with current optical network and data center technologies. This VCSEL-based spiking encoder paves the way toward optical spike-based data processing and ultrafast neuromorphic vision systems.
Highlights
In recent years, the fields of artificial intelligence (AI) and machine learning (ML) have been growing rapidly, fueling the interest and research efforts in unconventional, “beyond von Neumann” computing hardware
To assess the viability of the vertical-cavity surface emitting lasers (VCSELs)-neuron for encoding pixel color intensity information into optical spike trains, we investigate its operation using multiple input digital images
Similar to the stochastic nature of certain biological neurons, where spike firing can be considered as governed by Poisson statistics,39 the individual spiking events can appear at random instants while still correctly representing the input GS image data in the average spiking rate, which increases monotonically with the input pixel intensity
Summary
The fields of artificial intelligence (AI) and machine learning (ML) have been growing rapidly, fueling the interest and research efforts in unconventional, “beyond von Neumann” computing hardware. Current neuromorphic computing platforms include, among others, Loihi by Intel, SpiNNaker by the University of Manchester, TrueNorth by IBM, Akida by BrainChip, and BrainScaleS by the University of Heidelberg.5 These platforms exhibit a varying degree of biological plausibility and hold great promise for efficient operation of AI algorithms or computational neuroscientific models.. Besides the aforementioned fully electronic approaches, photonic realizations of neuromorphic hardware are attracting increasing interest, given the key inherent advantages of optical systems.11 Examples of these include signaling via optical pulses that have non-interacting bosonic nature and allow for both low-loss, high-speed waveguiding and wavelength-division multiplexing, which allows us to increase communication capacity. VCSELs exhibit the capability of information representation using spiking rate coding, as observed in certain classes of biological neurons This functionality yields them as a promising solution for interfacing and conversion of data into a suitable spike-based representation, a key challenge in the field of neuromorphic engineering.
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