Emerging Devices and Packaging Strategies for Electronic-Photonic AI Accelerators

TL;DR

This paper reviews emerging electronic-photonic device and packaging strategies that aim to enhance AI accelerators by combining photonics and electronics for improved performance and integration.

Contribution

It provides a comprehensive overview of new materials, devices, and packaging approaches for electronic-photonic neural network accelerators, highlighting their potential to advance AI hardware.

Findings

Photonic components offer high-speed signal processing capabilities.

Emerging electro-optic materials enable new device functionalities.

Packaging strategies are crucial for integrating photonics with electronics.

Abstract

The field of mimicking the structure of the brain on a chip is experiencing interest driven by the demand for machine intelligent applications. However, the power consumption and available performance of machine-learning (ML) accelerating hardware still leave much desire for improvement. In this letter, we share viewpoints, challenges, and prospects of electronic-photonic neural network (NN) accelerators. Combining electronics with photonics offers synergistic co-design strategies for high-performance AI Application-specific integrated circuits (ASICs) and systems. Taking advantages of photonic signal processing capabilities and combining them with electronic logic control and data storage is an emerging prospect. However, the optical component library leaves much to be desired and is challenged by the enormous size of photonic devices. Within this context, we will review the emerging electro-optic materials, functional devices, and systems packaging strategies that, when realized, provide significant performance gains and fuel the ongoing AI revolution, leading to a stand-alone photonics-inside AI ASIC 'black-box' for streamlined plug-and-play board integration in future AI processors.