Work

A 262 TOPS hyperdimensional photonic AI accelerator powered by a Si3N4 microcomb laser

Year: 2025

Type: article

Abstract: The ever-increasing volume of data demarcating from the exponential scale of Artificial Intelligence (AI) and Deep Learning (DL) models motivated research into specialized AI accelerators in order to complement digital processors. Photonic Neural Networks (PNNs), with their unique ability to capitalize on the interplay of multiple physical dimensions, including time, wavelength, and space, have been brought forward with a credible promise for boosting computational power and energy efficiency in AI processors. In this article, we experimentally demonstrate a novel multidimensional arrayed waveguide grating router (AWGR)-based photonic AI accelerator that can offload bandwidth-bounded linear algebra while leaving memory hierarchy, control, and nonlinearities to electronics and can execute tensor multiplications at a record-high total computational power of 262 TOPS, offering a ∼24× improvement over the existing waveguide-based optical accelerators. It consists of a 16 × 16 AWGR that exploits the time-, wavelength-, and space-division multiplexing (T-W-SDM) for weight and input encoding, together with an integrated Si3N4-based frequency comb for multi-wavelength generation. The photonic AI accelerator has been experimentally validated in both Fully Connected (FC) and Convolutional NN (CNN) models, with the FC and CNN being trained for DDoS attack identification and MNIST classification, respectively. The experimental inference at 32 Gbaud achieved a Cohen’s kappa score of 0.8652 for DDoS detection and an accuracy of 92.14% for MNIST classification, respectively, closely matching the software performance.

Source: APL Photonics

DOI: 10.1063/5.0271374