A spiking photonic neural network of 40.000 neurons, trained with rank-order coding for leveraging sparsity

• URL: http://arxiv.org/abs/2411.19209v2
• Date: Wed, 29 Jan 2025 12:04:40 GMT
• Title: A spiking photonic neural network of 40.000 neurons, trained with rank-order coding for leveraging sparsity
• Authors: Ria Talukder, Anas Skalli, Xavier Porte, Simon Thorpe, Daniel Brunner,
• Abstract summary: We present a photonic neural network (SNN) comprising 40,000 neurons using off-the-shelf components. The network achieves 83.5% accuracy on MNIST using 22% of neurons, and 77.5% with 8.5% neuron utilization. This demonstration integrates photonic nonlinearity, excitability, and sparse computation, paving the way for efficient large-scale photonic neuromorphic systems.
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• Abstract: piking neural networks are neuromorphic systems that emulate certain aspects of biological neurons, offering potential advantages in energy efficiency and speed by for example leveraging sparsity. While CMOS-based electronic SNN hardware has shown promise, scalability and parallelism challenges remain. Photonics provides a promising platform for SNNs due to the speed of excitable photonic devices standing in as neurons and the parallelism and low-latency of optical signal conduction. Here, we present a photonic SNN comprising 40,000 neurons using off-the-shelf components, including a spatial light modulator and a CMOS camera, enabling scalable and cost-effective implementations for photonic SNN proof of concept studies. The system is governed by a modified Ikeda map, were adding additional inhibitory feedback forcing introduces excitability akin to biological dynamics. Using latency encoding and sparsity, the network achieves 83.5% accuracy on MNIST using 22% of neurons, and 77.5% with 8.5% neuron utilization. Training is performed via liquid state machine concepts combined with the hardware-compatible SPSA algorithm, marking its first use in photonic neural networks. This demonstration integrates photonic nonlinearity, excitability, and sparse computation, paving the way for efficient large-scale photonic neuromorphic systems.

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