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DOE OSTI · 2248148

Fluid learning: Mimicking brain computing with neuromorphic nanofluidic devices

Noy, Aleksandr·Li, Zhongwu·Darling, Seth B.
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Abstract

Relentlessly rising energy demands in computing call for rethinking hardware paradigms with energy efficiency in mind. Nature’s example—the brain—raises the question: How can these natural computers achieve remarkable feats with minimal energy compared to supercomputers? Neuromorphic computing mimics the brain’s principles, but current neuromorphic concepts using electronic components face scalability and their own power consumption challenges. A potentially revolutionary approach is emerging: computing with ion transport in water through nanochannels. This field offers energy-efficient possibilities by imitating brain-like information processing with different types of ions as carriers. Finally, the goal is to converge advanced nanoscale architectures with brain-inspired efficiency, heralding a new era of computing.

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Noy, Aleksandr, Li, Zhongwu, Darling, Seth B.. 2023-10-26. Fluid learning: Mimicking brain computing with neuromorphic nanofluidic devices. https://doi.org/10.1016/j.nantod.2023.102043

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