A Survey on Silicon Photonics for Deep Learning

TL;DR

This survey explores how silicon photonics can overcome electronic bottlenecks in deep learning hardware, highlighting recent developments, capabilities, and limitations of photonics-based accelerators for AI workloads.

Contribution

It provides a comprehensive overview of silicon photonics applications in deep learning acceleration, emphasizing design abstractions and current challenges.

Findings

Silicon photonics offers promising solutions for high-bandwidth, energy-efficient deep learning hardware.

Current limitations include integration complexity and scalability issues.

Photonic accelerators can potentially surpass electronic counterparts in performance and efficiency.

Abstract

Deep learning has led to unprecedented successes in solving some very difficult problems in domains such as computer vision, natural language processing, and general pattern recognition. These achievements are the culmination of decades-long research into better training techniques and deeper neural network models, as well as improvements in hardware platforms that are used to train and execute the deep neural network models. Many application-specific integrated circuit (ASIC) hardware accelerators for deep learning have garnered interest in recent years due to their improved performance and energy-efficiency over conventional CPU and GPU architectures. However, these accelerators are constrained by fundamental bottlenecks due to 1) the slowdown in CMOS scaling, which has limited computational and performance-per-watt capabilities of emerging electronic processors, and 2) the use of metallic interconnects for data movement, which do not scale well and are a major cause of bandwidth, latency, and energy inefficiencies in almost every contemporary processor. Silicon photonics has emerged as a promising CMOS-compatible alternative to realize a new generation of deep learning accelerators that can use light for both communication and computation. This article surveys the landscape of silicon photonics to accelerate deep learning, with a coverage of developments across design abstractions in a bottom-up manner, to convey both the capabilities and limitations of the silicon photonics paradigm in the context of deep learning acceleration.