The road to commercial success for neuromorphic technologies

Dylan Richard Muir () and Sadique Sheik
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Dylan Richard Muir: SynSense
Sadique Sheik: SynSense

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-57352-1

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