Substantial improvements in computing energy efficiency, by up to ten orders of magnitude, will be required to solve major computing problems — such as planetary-scale weather modelling, real-time, brain-scale modelling and human evolutionary simulation — by the end of this century.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Tunable stochastic memristors for energy-efficient encryption and computing
Nature Communications Open Access 15 April 2024
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Silver, D. et al. Nature 529, 484–489 (2016).
Jumper, J. et al. Nature 596, 583–589 (2021).
Schwartz, R., Dodge, J., Smith, N. A. & Etzioni, O. Commun. ACM 63, 54–63 (2020).
Sevilla, J. et al. Compute trends across three eras of machine learning. In 2022 Int. Joint Conf. Neural Networks (IJCNN) 1–8 (IEEE, 2022); https://doi.org/10.1109/IJCNN55064.2022.9891914
Patterson, D. et al. Preprint at https://arxiv.org/abs/2104.10350 (2021).
Wiggers, K. OpenAI’s massive GPT-3 model is impressive, but size isn’t everything. VentureBeat https://go.nature.com/3NjpWWc (1 June 2020).
Strubell, E., Ganesh, A. & McCallum, A. Preprint at https://arxiv.org/abs/1906.02243 (2019).
Knight, W. OpenAI’s CEO says the age of giant AI models is already over. Wired https://go.nature.com/3CjLFao (17 April 2023).
DeBenedictis, E. P. Reversible logic for supercomputing. CF ‘05: Proc. 2nd Conf. Computing Front. 391–402 https://doi.org/10.1145/1062261.1062325 (ACM, 2005).
Kendall, J. D. & Kumar, S. Appl. Phys. Rev. 7, 011305 (2020).
Smith, C. M. How humans will evolve on multigenerational space exploration missions. Scientific American https://go.nature.com/3NgYatw (1 January 2013).
Yampolskiy, R. V. Evol. Bioinformat. 14, 1–11 (2018).
Bostrom, N. Philos. Q. 53, 243–255 (2003).
Sherry, Y. & Thompson, N. Proc. IEEE 109, 1768–1777 (2021).
Kumar, S. Preprint at https://arxiv.org/abs/1511.05956 (2015).
Yi, S.-i., Kendall, J. D., Williams, R. S. & Kumar, S. Nat. Electron. 6, 45–51 (2023).
Ercsey-Ravasz, M. & Toroczkai, Z. Nat. Phys. 7, 966–970 (2011).
Preskill, J. Quantum 2, 79 (2018).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Electronics thanks Yiyu Shi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Supplementary information
Supplementary Information
Supplementary Sections 1–8, Supplementary Table 1, and Supplementary Figs. 1 and 2.
Rights and permissions
About this article
Cite this article
Conklin, A.A., Kumar, S. Solving the big computing problems in the twenty-first century. Nat Electron 6, 464–466 (2023). https://doi.org/10.1038/s41928-023-00985-1
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41928-023-00985-1
This article is cited by
-
Tunable stochastic memristors for energy-efficient encryption and computing
Nature Communications (2024)
-
Some steps towards a safe and sustainable AI
Nature Electronics (2023)
