High-speed multiwavelength photonic temporal integration using silicon photonics

Optical systems have been pivotal for energy-efficient computing, performing high-speed, parallel operations in low-loss carriers. While these predominantly analog optical accelerators bypass digitization to perform parallel floating-point computations, scaling optical hardware to map large-vector sizes for AI tasks remains challenging. Here, we overcome this limitation by unfolding scalar operations in time and introducing a photonic-heater-in-lightpath (PHIL) unit for all-optical temporal integration. Counterintuitively, we exploit a slow heat dissipation process to integrate optical signals modulated at 50 GHz bridging the speed gap between the widely applied thermo-optic effects and ultrafast photonics. This architecture supports optical end-to-end signal processing, eliminates inefficient electro-optical conversions, and enables both linear and nonlinear operations within a unified framework. Our results demonstrate a scalable path towards high-speed photonic computing through thermally driven integration.

A 262 TOPS Hyperdimensional Photonic AI Accelerator powered by a Si3N4 microcomb laser

The ever-increasing volume of data has necessitated a new computing paradigm, embodied through Artificial Intelligence (AI) and Large Language Models (LLMs). Digital electronic AI computing systems, however, are gradually reaching their physical plateaus, stimulating extensive research towards next-generation AI accelerators. 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 execute tensor multiplications at a record-high total computational power of 262 TOPS, offering a ~24x improvement over the existing waveguide-based optical accelerators. It consists of a 16x16 AWGR that exploits the time-, wavelength- and space- division multiplexing (T-WSDM) 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 (NNs) 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 0.867 for DDoS detection and an accuracy of 92.14% for MNIST classification, respectively, closely matching the software performance.