Photonic-Electronic Integrated Circuits for High-Performance Computing and AI Accelerators

TL;DR

This paper reviews recent advances in photonic-electronic integrated circuits, highlighting their potential to overcome traditional computing limitations for AI and high-performance computing through low latency and high bandwidth optical technologies.

Contribution

It provides a comprehensive survey of PIC-based digital and analog computing for AI, analyzing benefits, challenges, and future strategies for optical computing integration.

Findings

Photonic circuits offer high bandwidth and low latency for AI computing.

Integration of photonics with electronics enhances performance and energy efficiency.

Challenges include fabrication complexity and system integration issues.

Abstract

In recent decades, the demand for computational power has surged, particularly with the rapid expansion of artificial intelligence (AI). As we navigate the post-Moore's law era, the limitations of traditional electrical digital computing, including process bottlenecks and power consumption issues, are propelling the search for alternative computing paradigms. Among various emerging technologies, integrated photonics stands out as a promising solution for next-generation high-performance computing, thanks to the inherent advantages of light, such as low latency, high bandwidth, and unique multiplexing techniques. Furthermore, the progress in photonic integrated circuits (PICs), which are equipped with abundant photoelectronic components, positions photonic-electronic integrated circuits as a viable solution for high-performance computing and hardware AI accelerators. In this review, we survey recent advancements in both PIC-based digital and analog computing for AI, exploring the principal benefits and obstacles of implementation. Additionally, we propose a comprehensive analysis of photonic AI from the perspectives of hardware implementation, accelerator architecture, and software-hardware co-design. In the end, acknowledging the existing challenges, we underscore potential strategies for overcoming these issues and offer insights into the future drivers for optical computing.