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arXiv · 2405.06434

Photonic Neuromorphic Accelerator for Convolutional Neural Networks based on an Integrated Reconfigurable Mesh

Aris Tsirigotis·Gerge Sarantoglou·Stavros Deligiannidis·Erica Sanchez·Ana Gutierrez·Adonis Bogris·Jose Capmany·Charis Mesaritakis
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Abstract

In this work, we present and experimentally validate a passive photonic-integrated neuromorphic accelerator that uses a hardware-friendly optical spectrum slicing technique through a reconfigurable silicon photonic mesh. The proposed scheme acts as an analogue convolutional engine, enabling information preprocessing in the optical domain, dimensionality reduction and extraction of spatio-temporal features. Numerical results demonstrate that utilizing only 7 passive photonic nodes, critical modules of a digital convolutional neural network can be replaced. As a result, a 98.6% accuracy on the MNIST dataset was achieved, with a power consumption reduction of at least 26% compared to digital CNNs. Experimental results confirm these findings, achieving 97.7% accuracy with only 3 passive nodes.

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Aris Tsirigotis, Gerge Sarantoglou, Stavros Deligiannidis, Erica Sanchez, Ana Gutierrez, Adonis Bogris, Jose Capmany, Charis Mesaritakis. 2024-05-10. Photonic Neuromorphic Accelerator for Convolutional Neural Networks based on an Integrated Reconfigurable Mesh. https://arxiv.org/abs/2405.06434

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