Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Comment
  • Published:

Neuromorphic pathways for transforming AI hardware

Nature Electronics volume 8, pages 752–756 (2025)Cite this article

Subjects

Neuromorphic hardware has typically focused on accelerating vector–matrix multiplication, but broader and more disruptive approaches will be required to reimagine AI hardware.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Underlying mathematical operations in neural network implementations.
Fig. 2: Neural network implementation on a neuromorphic dot-product engine.
Fig. 3: Noise in neuromorphic hardware.

References

  1. Jain, P. et al. Proc. Mach. Learn. Syst. 2, 497–511 (2020).

    Google Scholar 

  2. Chen, S. Nature https://doi.org/10.1038/d41586-025-01113-z (2025).

    Article  Google Scholar 

  3. Li, P., Yang, J., Islam, M. A. & Ren, S. Commun. ACM 68, 54–61 (2025).

  4. Cho, R. AI’s growing carbon footprint. State of the Planet https://go.nature.com/44JFsU6 (9 June 2023).

  5. Pultarova, T. Should we be moving data centers to space? MIT Technology Review https://go.nature.com/4lKs9Jf (3 March 2025).

  6. Le Gallo, M. et al. Nat. Electron. 6, 680–693 (2023).

    Article  Google Scholar 

  7. Peng, X., Huang, S., Jiang, H., Lu, A. & Yu, S. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 40, 2306–2319 (2021).

    Article  Google Scholar 

  8. Lee, S. et al. In MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture 131–142 (ACM, 2021); https://doi.org/10.1145/3466752.3480089

  9. Yao, P. et al. Nature 577, 641–646 (2020).

    Article  Google Scholar 

  10. Aguirre, F. et al. Nat. Commun. 15, 1974 (2024).

    Article  Google Scholar 

  11. Sharma, D. et al. Nature 633, 560–566 (2024).

    Article  Google Scholar 

  12. Noise Analysis in Operational Amplifier Circuits Application Report SLVA043B (Texas Instruments, 2007).

  13. Song, W. et al. Science 383, 903–910 (2024).

    Article  MathSciNet  Google Scholar 

  14. Liao, Y. et al. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 40, 1662–1671 (2021).

    Article  Google Scholar 

  15. Wan, W. et al. Nature 608, 504–512 (2022).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Science, Bengaluru, India

    Harivignesh S, Navakanta Bhat & Sreetosh Goswami

Authors
  1. Harivignesh S
    View author publications

    Search author on:PubMed Google Scholar

  2. Navakanta Bhat
    View author publications

    Search author on:PubMed Google Scholar

  3. Sreetosh Goswami
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Sreetosh Goswami.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

S, H., Bhat, N. & Goswami, S. Neuromorphic pathways for transforming AI hardware. Nat Electron 8, 752–756 (2025). https://doi.org/10.1038/s41928-025-01432-z

Download citation

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41928-025-01432-z

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing