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Neuromorphic computing

Efficient large language model with analog in-memory computing

A recent study demonstrates through numerical simulations that implementing large language models based on sparse mixture-of-experts architectures on 3D in-memory computing technologies can substantially reduce energy consumption.

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Fig. 1: Mapping of a sparse mixture-of-experts model to 3D NVM analog in-memory computing.
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References

  1. Valinsky, J. Three Mile Island is reopening and selling its power to Microsoft. CNN Business https://edition.cnn.com/2024/09/20/energy/three-mile-island-microsoft-ai/index.html (2024).

  2. Büchel, J. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-024-00753-x (2025).

    Article  Google Scholar 

  3. Verma, N. et al. IEEE Solid-State Circuits Mag. 11, 43–55 (2019).

    Article  Google Scholar 

  4. Yang, J. J., Strukov, D. B. & Stewart, D. R. Nat. Nanotechnol. 8, 13–24 (2013).

    Article  Google Scholar 

  5. Fedus, W., Zoph, B. & Shazeer, N. J. Mach. Learn. Res. 23, 1–39 (2022).

    Google Scholar 

  6. Jiang, A. Q. et al. Preprint at https://doi.org/10.48550/arXiv.2401.04088 (2024).

  7. Joshi, V. et al. Nat. Commun. 11, 2473 (2020).

    Article  Google Scholar 

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Correspondence to Anand Subramoney.

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Subramoney, A. Efficient large language model with analog in-memory computing. Nat Comput Sci 5, 5–6 (2025). https://doi.org/10.1038/s43588-024-00760-y

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