---
OA_place: publisher
OA_type: hybrid
_id: '21554'
abstract:
- lang: eng
text: 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.
article_number: '012002'
article_processing_charge: No
article_type: original
author:
- first_name: Karl
full_name: Berggren, Karl
last_name: Berggren
- first_name: Qiangfei
full_name: Xia, Qiangfei
last_name: Xia
- first_name: Konstantin K
full_name: Likharev, Konstantin K
last_name: Likharev
- first_name: Dmitri B
full_name: Strukov, Dmitri B
last_name: Strukov
- first_name: Hao
full_name: Jiang, Hao
last_name: Jiang
- first_name: Thomas
full_name: Mikolajick, Thomas
last_name: Mikolajick
- first_name: Damien
full_name: Querlioz, Damien
last_name: Querlioz
- first_name: Martin
full_name: Salinga, Martin
last_name: Salinga
- first_name: John R
full_name: Erickson, John R
last_name: Erickson
- first_name: Shuang
full_name: Pi, Shuang
last_name: Pi
- first_name: Feng
full_name: Xiong, Feng
last_name: Xiong
- first_name: Peng
full_name: Lin, Peng
last_name: Lin
- first_name: Can
full_name: Li, Can
last_name: Li
- first_name: Yu
full_name: Chen, Yu
last_name: Chen
- first_name: Shisheng
full_name: Xiong, Shisheng
last_name: Xiong
- first_name: Brian D
full_name: Hoskins, Brian D
last_name: Hoskins
- first_name: Matthew W
full_name: Daniels, Matthew W
last_name: Daniels
- first_name: Advait
full_name: Madhavan, Advait
last_name: Madhavan
- first_name: James A
full_name: Liddle, James A
last_name: Liddle
- first_name: Jabez J
full_name: McClelland, Jabez J
last_name: McClelland
- first_name: Yuchao
full_name: Yang, Yuchao
last_name: Yang
- first_name: Jennifer
full_name: Rupp, Jennifer
last_name: Rupp
- first_name: Stephen S
full_name: Nonnenmann, Stephen S
last_name: Nonnenmann
- first_name: Kwang-Ting
full_name: Cheng, Kwang-Ting
last_name: Cheng
- first_name: Nanbo
full_name: Gong, Nanbo
last_name: Gong
- first_name: Miguel Angel
full_name: Lastras-Montaño, Miguel Angel
last_name: Lastras-Montaño
- first_name: A Alec
full_name: Talin, A Alec
last_name: Talin
- first_name: Alberto
full_name: Salleo, Alberto
last_name: Salleo
- first_name: Bhavin J
full_name: Shastri, Bhavin J
last_name: Shastri
- first_name: Thomas Ferreira
full_name: de Lima, Thomas Ferreira
last_name: de Lima
- first_name: Paul
full_name: Prucnal, Paul
last_name: Prucnal
- first_name: Alexander N
full_name: Tait, Alexander N
last_name: Tait
- first_name: Yichen
full_name: Shen, Yichen
last_name: Shen
- first_name: Huaiyu
full_name: Meng, Huaiyu
last_name: Meng
- first_name: Charles
full_name: Roques-Carmes, Charles
id: e2e68fc9-6505-11ef-a541-eb4e72cc3e82
last_name: Roques-Carmes
- first_name: Zengguang
full_name: Cheng, Zengguang
last_name: Cheng
- first_name: Harish
full_name: Bhaskaran, Harish
last_name: Bhaskaran
- first_name: Deep
full_name: Jariwala, Deep
last_name: Jariwala
- first_name: Han
full_name: Wang, Han
last_name: Wang
- first_name: Jeffrey M
full_name: Shainline, Jeffrey M
last_name: Shainline
- first_name: Kenneth
full_name: Segall, Kenneth
last_name: Segall
- first_name: J Joshua
full_name: Yang, J Joshua
last_name: Yang
- first_name: Kaushik
full_name: Roy, Kaushik
last_name: Roy
- first_name: Suman
full_name: Datta, Suman
last_name: Datta
- first_name: Arijit
full_name: Raychowdhury, Arijit
last_name: Raychowdhury
citation:
ama: Berggren K, Xia Q, Likharev KK, et al. Roadmap on emerging hardware and technology
for machine learning. Nanotechnology. 2020;32(1). doi:10.1088/1361-6528/aba70f
apa: Berggren, K., Xia, Q., Likharev, K. K., Strukov, D. B., Jiang, H., Mikolajick,
T., … Raychowdhury, A. (2020). Roadmap on emerging hardware and technology for
machine learning. Nanotechnology. IOP Publishing. https://doi.org/10.1088/1361-6528/aba70f
chicago: Berggren, Karl, Qiangfei Xia, Konstantin K Likharev, Dmitri B Strukov,
Hao Jiang, Thomas Mikolajick, Damien Querlioz, et al. “Roadmap on Emerging Hardware
and Technology for Machine Learning.” Nanotechnology. IOP Publishing, 2020.
https://doi.org/10.1088/1361-6528/aba70f.
ieee: K. Berggren et al., “Roadmap on emerging hardware and technology for
machine learning,” Nanotechnology, vol. 32, no. 1. IOP Publishing, 2020.
ista: Berggren K, Xia Q, Likharev KK, Strukov DB, Jiang H, Mikolajick T, Querlioz
D, Salinga M, Erickson JR, Pi S, Xiong F, Lin P, Li C, Chen Y, Xiong S, Hoskins
BD, Daniels MW, Madhavan A, Liddle JA, McClelland JJ, Yang Y, Rupp J, Nonnenmann
SS, Cheng K-T, Gong N, Lastras-Montaño MA, Talin AA, Salleo A, Shastri BJ, de
Lima TF, Prucnal P, Tait AN, Shen Y, Meng H, Roques-Carmes C, Cheng Z, Bhaskaran
H, Jariwala D, Wang H, Shainline JM, Segall K, Yang JJ, Roy K, Datta S, Raychowdhury
A. 2020. Roadmap on emerging hardware and technology for machine learning. Nanotechnology.
32(1), 012002.
mla: Berggren, Karl, et al. “Roadmap on Emerging Hardware and Technology for Machine
Learning.” Nanotechnology, vol. 32, no. 1, 012002, IOP Publishing, 2020,
doi:10.1088/1361-6528/aba70f.
short: K. Berggren, Q. Xia, K.K. Likharev, D.B. Strukov, H. Jiang, T. Mikolajick,
D. Querlioz, M. Salinga, J.R. Erickson, S. Pi, F. Xiong, P. Lin, C. Li, Y. Chen,
S. Xiong, B.D. Hoskins, M.W. Daniels, A. Madhavan, J.A. Liddle, J.J. McClelland,
Y. Yang, J. Rupp, S.S. Nonnenmann, K.-T. Cheng, N. Gong, M.A. Lastras-Montaño,
A.A. Talin, A. Salleo, B.J. Shastri, T.F. de Lima, P. Prucnal, A.N. Tait, Y. Shen,
H. Meng, C. Roques-Carmes, Z. Cheng, H. Bhaskaran, D. Jariwala, H. Wang, J.M.
Shainline, K. Segall, J.J. Yang, K. Roy, S. Datta, A. Raychowdhury, Nanotechnology
32 (2020).
date_created: 2026-03-30T12:22:47Z
date_published: 2020-10-19T00:00:00Z
date_updated: 2026-04-15T06:55:27Z
day: '19'
ddc:
- '530'
doi: 10.1088/1361-6528/aba70f
extern: '1'
external_id:
pmid:
- '32679577'
intvolume: ' 32'
issue: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1088/1361-6528/aba70f
month: '10'
oa: 1
oa_version: Published Version
pmid: 1
publication: Nanotechnology
publication_identifier:
eissn:
- 1361-6528
issn:
- 0957-4484
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Roadmap on emerging hardware and technology for machine learning
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 32
year: '2020'
...