A Survey on Silicon Photonics for Deep Learning

被引:42
|
作者
Sunny, Febin P. [1 ]
Taheri, Ebadollah [1 ]
Nikdast, Mahdi [1 ]
Pasricha, Sudeep [1 ]
机构
[1] Colorado State Univ, Dept Elect & Comp Engn, Ft Collins, CO 80523 USA
关键词
Silicon photonics; deep learning; neuromorphic computing; TIMING-DEPENDENT PLASTICITY; MICRODISK LASERS; NEURAL-NETWORKS; NEURONS; DESIGN; IMPLEMENTATION; NONLINEARITY; POLARIZATION; EXCITABILITY; ACCELERATOR;
D O I
10.1145/3459009
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:57
相关论文
共 50 条
  • [41] Survey on Blockchain and Deep Learning
    Zhang, Yizhuo
    Liu, Yiwei
    Chen, Chi-Hua
    [J]. 2020 IEEE 19TH INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (TRUSTCOM 2020), 2020, : 1989 - 1994
  • [42] A survey: evolutionary deep learning
    Yifan Li
    Jing Liu
    [J]. Soft Computing, 2023, 27 : 9401 - 9423
  • [43] Evolutionary deep learning: A survey
    Zhan, Zhi-Hui
    Li, Jian-Yu
    Zhang, Jun
    [J]. NEUROCOMPUTING, 2022, 483 : 42 - 58
  • [44] A Survey of Deep Active Learning
    Ren, Pengzhen
    Xiao, Yun
    Chang, Xiaojun
    Huang, Po-Yao
    Li, Zhihui
    Gupta, Brij B.
    Chen, Xiaojiang
    Wang, Xin
    [J]. ACM COMPUTING SURVEYS, 2022, 54 (09)
  • [45] A survey: evolutionary deep learning
    Li, Yifan
    Liu, Jing
    [J]. SOFT COMPUTING, 2023, 27 (14) : 9401 - 9423
  • [46] Deep Learning for Biometrics: A Survey
    Sundararajan, Kalaivani
    Woodard, Damon L.
    [J]. ACM COMPUTING SURVEYS, 2018, 51 (03)
  • [47] Deep learning in nano-photonics: inverse design and beyond
    PETER R.WIECHA
    ARNAUD ARBOUET
    CHRISTIAN GIRARD
    OTTO L.MUSKENS
    [J]. Photonics Research, 2021, 9 (05) : 584 - 602
  • [48] Deep Metric Learning: A Survey
    Kaya, Mahmut
    Bilge, Hasan Sakir
    [J]. SYMMETRY-BASEL, 2019, 11 (09):
  • [49] Deep Learning on Graphs: A Survey
    Zhang, Ziwei
    Cui, Peng
    Zhu, Wenwu
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2022, 34 (01) : 249 - 270
  • [50] A Survey on Deep Transfer Learning
    Tan, Chuanqi
    Sun, Fuchun
    Kong, Tao
    Zhang, Wenchang
    Yang, Chao
    Liu, Chunfang
    [J]. ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III, 2018, 11141 : 270 - 279