Realisation of large-scale photonic spiking hardware system

Ria Talukder; Xavier Porte; Daniel Brunner

doi:10.1117/12.2633530

3 October 2022 Realisation of large-scale photonic spiking hardware system

Ria Talukder, Xavier Porte, Daniel Brunner

Proceedings Volume 12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; 122040A (2022) https://doi.org/10.1117/12.2633530
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

An efficient photonic hardware integration of neural networks can benefit us from the inherent properties of parallelism, high-speed data processing and potentially low energy consumption. In artificial neural networks (ANN), neurons are classified as static, single and continuous-valued. On contrary, information transmission and computation in biological neurons occur through spikes, where spike time and rate play a significant role. Spiking neural networks (SNNs) are thereby more biologically relevant along with additional benefits in terms of hardware friendliness and energy-efficiency. Considering all these advantages, we designed a photonic reservoir computer (RC) based on photonic recurrent spiking neural networks (SNN) i.e. a liquid state machine. It is a scalable proof-of-concept experiment, comprising more than 30,000 neurons. This system presents an excellent testbed for demonstrating next generation bio-inspired learning in photonic systems.

Conference Presentation

Citation Download Citation

Ria Talukder, Xavier Porte, and Daniel Brunner "Realisation of large-scale photonic spiking hardware system", Proc. SPIE 12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, 122040A (3 October 2022); https://doi.org/10.1117/12.2633530

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