Fluid learning: Mimicking brain computing with neuromorphic nanofluidic devices

Noy, Aleksandr; Li, Zhongwu; Darling, Seth B.

doi:10.1016/j.nantod.2023.102043

Fluid learning: Mimicking brain computing with neuromorphic nanofluidic devices

Journal Article · Thu Oct 26 00:00:00 EDT 2023 · Nano Today

DOI:https://doi.org/10.1016/j.nantod.2023.102043· OSTI ID:2248148

^[1]; Li, Zhongwu ^[2]; Darling, Seth B. ^[3]

Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Univ. of California, Merced, CA (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-06CH11357; AC52-07NA27344; SC0019112

OSTI ID:: 2248148

Alternate ID(s):: OSTI ID: 2356825

Report Number(s):: LLNL--JRNL-851463; 1078332

Journal Information:: Nano Today, Journal Name: Nano Today Vol. 53; ISSN 1748-0132

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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