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The brain is a diverse place, why not computing? – Nature Machine Intelligence
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Neuromorphic architectures
Nature Machine Intelligence (2026) Cite this article
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The brain’s architecture exhibits diversity across many temporal and spatial scales, yet our computing architectures remain largely homogeneous. Low-powered neuromorphic hardware offers a path towards energy-efficient AI, but could these approaches be improved with heterogeneous computing architectures?
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Acknowledgements
This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under contract no. DE-NA0003525 with the US Department of Energy (DOE). The employee owns all right, title and interest in and to the article and is solely responsible for its contents. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this article or allow others to do so, for US Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan https://www.energy.gov/downloads/doe-public-access-plan.
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Chandarana, P., Aimone, J.B. The brain is a diverse place, why not computing?. Nat Mach Intell (2026). https://doi.org/10.1038/s42256-026-01273-1
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DOI: https://doi.org/10.1038/s42256-026-01273-1
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