Photonic neuromorphic accelerator for convolutional neural networks based on an integrated reconfigurable mesh
Aris Tsirigotis, George Sarantoglou, Stavros Deligiannidis, Erica Sánchez, David Sanchez, Ana Gutiérrez, Adonis Bogris, J. Capmany, Charis Mesaritakis · Communications Engineering · 2025
Photonic accelerators have risen as energy efficient, low latency counterparts to computational hungry digital modules for machine learning applications. On the other hand, upscaling integrated photonic circuits to meet the demands of state-of-the-art machine learning schemes such as convolutional layers, remains challenging. In this work, we experimentally validate a 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 with only 7 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 numerically achieved, with an estimation of power consumption reduction up to 30% compared to digital convolutional neural networks. Experimental results using a reconfigurable silicon integrated chip confirm these findings, achieving 97.7% accuracy with only three optical nodes. Aris Tsirigotis and colleagues propose a photonic neuromorphic accelerator using optical spectrum slicing in a reconfigurable processor. Their approach enables optical-domain preprocessing, achieving 97.7% accuracy on MNIST with up to 30% lower power consumption than digital systems.