A photonic AI Accelerator is a hardware system that uses optical components such as waveguides, modulators, and interferometers to perform key AI computations, particularly matrix multiplications, using light instead of electrical signals. By leveraging parallelism and low propagation loss, photonic accelerators can achieve high throughput and energy efficiency for neural network inference and training. They are used in data centers, edge systems, and high-performance computing to accelerate machine learning workloads.

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Emerging Integrated Photonic Technologies Leveraging Multimaterial Integration for AI and Datacenter Applications

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Since the inception of integrated photonics, multimaterial integration has served as a primary avenue for new technology innovations. Now, with an ever-increasing demand for integrated photonics as a platform for both high-performance links from/within datacenters and AI acceleration, multimaterial integration has begun to play an even more critical role in pushing capabilities beyond their current limits. In this work, we review photonics for AI and datacenter applications, the current landscape of multimaterial integration in photonics, and the ways in which multimaterial integration techniques have been recently utilized to push the performance of modulators on silicon and chip-scale optical frequency combs.

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