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The road to commercial success for neuromorphic technologies

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  • Dylan Richard Muir

    (SynSense
    University of Western Australia)

  • Sadique Sheik

    (SynSense
    Unique)

Abstract

Neuromorphic technologies adapt biological neural principles to synthesise high-efficiency computational devices, characterised by continuous real-time operation and sparse event-based communication. After several false starts, a confluence of advances now promises widespread commercial adoption. Gradient-based training of deep spiking neural networks is now an off-the-shelf technique for building general-purpose Neuromorphic applications, with open-source tools underwritten by theoretical results. Analog and mixed-signal Neuromorphic circuit designs are being replaced by digital equivalents in newer devices, simplifying application deployment while maintaining computational benefits. Designs for in-memory computing are also approaching commercial maturity. Solving two key problems—how to program general Neuromorphic applications; and how to deploy them at scale—clears the way to commercial success of Neuromorphic processors. Ultra-low-power Neuromorphic technology will find a home in battery-powered systems, local compute for internet-of-things devices, and consumer wearables. Inspiration from uptake of tensor processors and GPUs can help the field overcome remaining hurdles.

Suggested Citation

  • Dylan Richard Muir & Sadique Sheik, 2025. "The road to commercial success for neuromorphic technologies," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57352-1
    DOI: 10.1038/s41467-025-57352-1
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