Model-free front-to-end training of a large high performance laser neural network

• URL: http://arxiv.org/abs/2503.16943v1
• Date: Fri, 21 Mar 2025 08:43:02 GMT
• Title: Model-free front-to-end training of a large high performance laser neural network
• Authors: Anas Skalli, Satoshi Sunada, Mirko Goldmann, Marcin Gebski, Stephan Reitzenstein, James A. Lott, Tomasz Czyszanowski, Daniel Brunner,
• Abstract summary: We demonstrate a fully autonomous and parallel optical neural network (ONN) using off-the-shelf components.<n>Our ONN is highly efficient and is scalable both in network size and inference bandwidth towards the GHz range.<n>We show that our ONN can achieve high accuracy and convergence efficiency, even under limited hardware resources.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial neural networks (ANNs), have become ubiquitous and revolutionized many applications ranging from computer vision to medical diagnoses. However, they offer a fundamentally connectionist and distributed approach to computing, in stark contrast to classical computers that use the von Neumann architecture. This distinction has sparked renewed interest in developing unconventional hardware to support more efficient implementations of ANNs, rather than merely emulating them on traditional systems. Photonics stands out as a particularly promising platform, providing scalability, high speed, energy efficiency, and the ability for parallel information processing. However, fully realized autonomous optical neural networks (ONNs) with in-situ learning capabilities are still rare. In this work, we demonstrate a fully autonomous and parallel ONN using a multimode vertical cavity surface emitting laser (VCSEL) using off-the-shelf components. Our ONN is highly efficient and is scalable both in network size and inference bandwidth towards the GHz range. High performance hardware-compatible optimization algorithms are necessary in order to minimize reliance on external von Neumann computers to fully exploit the potential of ONNs. As such we present and extensively study several algorithms which are broadly compatible with a wide range of systems. We then apply these algorithms to optimize our ONN, and benchmark them using the MNIST dataset. We show that our ONN can achieve high accuracy and convergence efficiency, even under limited hardware resources. Crucially, we compare these different algorithms in terms of scaling and optimization efficiency in term of convergence time which is crucial when working with limited external resources. Our work provides some guidance for the design of future ONNs as well as a simple and flexible way to train them.

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