Channel response-aware photonic neural network accelerators for high-speed inference through bandwidth-limited optics
George Mourgias-Alexandris, Miltiadis Moralis‐Pegios, Apostolos Tsakyridis, Nikolaos Passalis, Manos Kirtas, Anastasios Tefas, Teerapat Rutirawut, Frédéric Y. Gardes, Nikos Pleros
Abstract
Photonic neural network accelerators (PNNAs) have been lately brought into the spotlight as a new class of custom hardware that can leverage the maturity of photonic integration towards addressing the low-energy and computational power requirements of deep learning (DL) workloads. Transferring, however, the high-speed credentials of photonic circuitry into analogue neuromorphic computing necessitates a new set of DL training methods aligned along certain analogue photonic hardware characteristics. Herein, we present a novel channel response-aware (CRA) DL architecture that can address the implementation challenges of high-speed compute rates on bandwidth-limited photonic devices by incorporating their frequency response into the training procedure. The proposed architecture was validated both through software and experimentally by implementing the output layer of a neural network (NN) that classifies images of the MNIST dataset on an integrated SiPho coherent linear neuron (COLN) with a 3dB channel bandwidth of 7 GHz. A comparative analysis between the baseline and CRA model at 20, 25 and 32GMAC/sec/axon revealed respective experimental accuracies of 98.5%, 97.3% and 92.1% for the CRA model, outperforming the baseline model by 7.9%, 12.3% and 15.6%, respectively.