A recent study demonstrates the potential of using in-memory computing architecture for implementing large language models for an improved computational efficiency in both time and energy while maintaining a high accuracy.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 / 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 SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
de Vries, A. Joule 7, 2191–2194 (2023).
Leroux, N. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-025-00854-1 (2025).
Horowitz, M. Computing’s energy problem (and what we can do about it). In Proc. 2014 IEEE International Solid-State Circuits Conference 10–14 (IEEE, 2014).
Liu, Z. et al. Scissorhands: exploiting the persistence of importance hypothesis for LLM KV cache compression at test time. In Proc. Advances in Neural Information Processing Systems (eds Oh, A. et al.) 52342–52364 (NeurIPS, 2023).
Wan, W. et al. Nature 608, 504–512 (2022).
Yao, P. et al. Nature 577, 641–646 (2020).
Lin, Y. et al. Nat. Comput. Sci. 5, 27–36 (2025).
Yang, X., Yan, B., Li, H. & Chen, Y. ReTransformer: ReRAM-based processing-in-memory architecture for transformer acceleration. In Proc. 39th International Conference on Computer-Aided Design (Association for Computing Machinery, 2020).
Yang, H. et al. Monolithic 3D integration of analog RRAM-based fully weight stationary and novel CFET 2T0C-based partially weight stationary for accelerating transformer. In Proc. 2024 IEEE Symposium on VLSI Technology and Circuits (IEEE, 2024).
Sridharan, S., Stevens, J. R., Roy, K. & Raghunathan, A. IEEE Trans. Very Large Scale Integr. VLSI Syst. 31, 1223–1233 (2023).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Lin, Y., Tang, J. Overcoming computational bottlenecks in large language models through analog in-memory computing. Nat Comput Sci 5, 711–712 (2025). https://doi.org/10.1038/s43588-025-00860-3
Published:
Version of record:
Issue date:
DOI: https://doi.org/10.1038/s43588-025-00860-3