Roadmap on emerging hardware and technology for machine learning
Karl K Berggren
1
,
Qiangfei Xia
2
,
Konstantin K. Likharev
3
,
Dmitri Strukov
4
,
Hao Jiang
5
,
Damien Querlioz
7
,
Martin Salinga
8
,
John R Erickson
9
,
Shuang Pi
10
,
Feng Xiong
9
,
Peng Lin
1
,
C X Li
11
,
Yu Chen
12
,
Shisheng Xiong
12
,
Brian D. Hoskins
13
,
Matthew W. Daniels
13
,
Advait Madhavan
13, 14
,
James A Liddle
13
,
Jabez J. McClelland
13
,
Yuchao Yang
15
,
Jennifer L. Rupp
16, 17
,
Stephen S. Nonnenmann
18
,
Kwang-Ting Cheng
19
,
N Gong
20
,
M A Lastras Montaño
21
,
Alec Talin
22
,
Alberto Salleo
23
,
Bhavin J. Shastri
24
,
Thomas Ferreira de Lima
25
,
Paul Prucnal
25
,
Alexander N. Tait
26
,
Yichen Shen
27
,
Huaiyu Meng
27
,
Charles Roques-Carmes
1
,
Z. Cheng
28, 29
,
H. Bhaskaran
30
,
Deep Jariwala
31
,
Hao-Zhe Wang
32
,
Jeffrey M. Shainline
26
,
Kenneth Segall
33
,
Jyisy Yang
2
,
Kaushik Roy
34
,
S. Datta
35
,
A. Raychowdhury
36
10
Lam Research, Fremont, CA, United States of America
|
12
27
Lightelligence, 268 Summer Street, Boston, MA 02210, United States of America
|
33
Department of Physics and Astronomy, Colgate University, NY 13346, United States of America
|
Publication type: Journal Article
Publication date: 2020-10-20
scimago Q2
wos Q2
SJR: 0.597
CiteScore: 6.2
Impact factor: 2.8
ISSN: 09574484, 13616528
PubMed ID:
32679577
General Chemistry
General Materials Science
Electrical and Electronic Engineering
Mechanical Engineering
Bioengineering
Mechanics of Materials
Abstract
Recent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.
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153
Total citations:
153
Citations from 2024:
56
(36.84%)
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Cite this
GOST
Copy
Berggren K. K. et al. Roadmap on emerging hardware and technology for machine learning // Nanotechnology. 2020. Vol. 32. No. 1. p. 12002.
GOST all authors (up to 50)
Copy
Berggren K. K., Xia Q., Likharev K. K., Strukov D., Jiang H., Mikolajick T., Querlioz D., Salinga M., Erickson J. R., Pi S., Xiong F., Lin P., Li C. X., Chen Yu., Xiong S., Hoskins B. D., Daniels M. W., Madhavan A., Liddle J. A., McClelland J. J., Yang Y., Rupp J. L., Nonnenmann S. S., Cheng K., Gong N., Lastras Montaño M. A., Talin A., Salleo A., Shastri B. J., Ferreira de Lima T., Prucnal P., Tait A. N., Shen Y., Meng H., Roques-Carmes C., Cheng Z., Bhaskaran H., Jariwala D., Wang H., Shainline J. M., Segall K., Yang J., Roy K., Datta S., Raychowdhury A. Roadmap on emerging hardware and technology for machine learning // Nanotechnology. 2020. Vol. 32. No. 1. p. 12002.
Cite this
RIS
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TY - JOUR
DO - 10.1088/1361-6528/aba70f
UR - https://doi.org/10.1088/1361-6528/aba70f
TI - Roadmap on emerging hardware and technology for machine learning
T2 - Nanotechnology
AU - Berggren, Karl K
AU - Xia, Qiangfei
AU - Likharev, Konstantin K.
AU - Strukov, Dmitri
AU - Jiang, Hao
AU - Mikolajick, Thomas
AU - Querlioz, Damien
AU - Salinga, Martin
AU - Erickson, John R
AU - Pi, Shuang
AU - Xiong, Feng
AU - Lin, Peng
AU - Li, C X
AU - Chen, Yu
AU - Xiong, Shisheng
AU - Hoskins, Brian D.
AU - Daniels, Matthew W.
AU - Madhavan, Advait
AU - Liddle, James A
AU - McClelland, Jabez J.
AU - Yang, Yuchao
AU - Rupp, Jennifer L.
AU - Nonnenmann, Stephen S.
AU - Cheng, Kwang-Ting
AU - Gong, N
AU - Lastras Montaño, M A
AU - Talin, Alec
AU - Salleo, Alberto
AU - Shastri, Bhavin J.
AU - Ferreira de Lima, Thomas
AU - Prucnal, Paul
AU - Tait, Alexander N.
AU - Shen, Yichen
AU - Meng, Huaiyu
AU - Roques-Carmes, Charles
AU - Cheng, Z.
AU - Bhaskaran, H.
AU - Jariwala, Deep
AU - Wang, Hao-Zhe
AU - Shainline, Jeffrey M.
AU - Segall, Kenneth
AU - Yang, Jyisy
AU - Roy, Kaushik
AU - Datta, S.
AU - Raychowdhury, A.
PY - 2020
DA - 2020/10/20
PB - IOP Publishing
SP - 12002
IS - 1
VL - 32
PMID - 32679577
SN - 0957-4484
SN - 1361-6528
ER -
Cite this
BibTex (up to 50 authors)
Copy
@article{2020_Berggren,
author = {Karl K Berggren and Qiangfei Xia and Konstantin K. Likharev and Dmitri Strukov and Hao Jiang and Thomas Mikolajick and Damien Querlioz and Martin Salinga and John R Erickson and Shuang Pi and Feng Xiong and Peng Lin and C X Li and Yu Chen and Shisheng Xiong and Brian D. Hoskins and Matthew W. Daniels and Advait Madhavan and James A Liddle and Jabez J. McClelland and Yuchao Yang and Jennifer L. Rupp and Stephen S. Nonnenmann and Kwang-Ting Cheng and N Gong and M A Lastras Montaño and Alec Talin and Alberto Salleo and Bhavin J. Shastri and Thomas Ferreira de Lima and Paul Prucnal and Alexander N. Tait and Yichen Shen and Huaiyu Meng and Charles Roques-Carmes and Z. Cheng and H. Bhaskaran and Deep Jariwala and Hao-Zhe Wang and Jeffrey M. Shainline and Kenneth Segall and Jyisy Yang and Kaushik Roy and S. Datta and A. Raychowdhury},
title = {Roadmap on emerging hardware and technology for machine learning},
journal = {Nanotechnology},
year = {2020},
volume = {32},
publisher = {IOP Publishing},
month = {oct},
url = {https://doi.org/10.1088/1361-6528/aba70f},
number = {1},
pages = {12002},
doi = {10.1088/1361-6528/aba70f}
}
Cite this
MLA
Copy
Berggren, Karl K., et al. “Roadmap on emerging hardware and technology for machine learning.” Nanotechnology, vol. 32, no. 1, Oct. 2020, p. 12002. https://doi.org/10.1088/1361-6528/aba70f.