The road to commercial success for neuromorphic technologies
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 ado...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57352-1 |
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| _version_ | 1850145086155784192 |
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| author | Dylan Richard Muir Sadique Sheik |
| author_facet | Dylan Richard Muir Sadique Sheik |
| author_sort | Dylan Richard Muir |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0ed5d817fbaf4c9aa25282d532ae4d4f |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-0ed5d817fbaf4c9aa25282d532ae4d4f2025-08-20T02:28:10ZengNature PortfolioNature Communications2041-17232025-04-0116111210.1038/s41467-025-57352-1The road to commercial success for neuromorphic technologiesDylan Richard Muir0Sadique Sheik1SynSenseSynSenseAbstract 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.https://doi.org/10.1038/s41467-025-57352-1 |
| spellingShingle | Dylan Richard Muir Sadique Sheik The road to commercial success for neuromorphic technologies Nature Communications |
| title | The road to commercial success for neuromorphic technologies |
| title_full | The road to commercial success for neuromorphic technologies |
| title_fullStr | The road to commercial success for neuromorphic technologies |
| title_full_unstemmed | The road to commercial success for neuromorphic technologies |
| title_short | The road to commercial success for neuromorphic technologies |
| title_sort | road to commercial success for neuromorphic technologies |
| url | https://doi.org/10.1038/s41467-025-57352-1 |
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