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A Survey on Silicon Photonics for Deep Learning

Authors:

Febin P. Sunny,

Ebadollah Taheri,

Sudeep PasrichaAuthors Info & Claims

ACM Journal of Emerging Technologies in Computing System, Volume 17, Issue 4

Article No.: 61, Pages 1 - 57

https://doi.org/10.1145/3459009

Published: 30 June 2021 Publication History

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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.

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Index Terms

A Survey on Silicon Photonics for Deep Learning
1. Computing methodologies
  1. Machine learning
2. Hardware
  1. Emerging technologies
    1. Emerging optical and photonic technologies
  2. Very large scale integration design
    1. Application-specific VLSI designs

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cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 17, Issue 4

October 2021

446 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/3472280

Editor:
Ramesh Karri
Polytechnic Institute of New York University, USA

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Accepted: 01 March 2021

Revised: 01 March 2021

Received: 01 January 2021

Published in JETC Volume 17, Issue 4

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https://doi.org/10.23919/DATE58400.2024.10546876
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