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Conference Papers Year : 2021

Energy Efficient Neuromorphic Computing with Oscillatory Neural Networks

Aida Todri-Sanial

Abstract

Current classical computers are playing a critical role in advanced research such as in biology, climate analysis, economics, genomics, finance, etc. In many aspects, computing fuels the advances of our modern society. Yet, recent developments in artificial intelligence (AI) and machine learning will require even more powerful computing systems such as exascale computations per second due to an ever-increasing amount of data. But classical computing systems are hindered by the von-Neumann communication bottleneck, the physical separation between processor and memory. This offers the opportunity to explore a novel computing paradigm where the brain can serve as a computational model of how to deal with large amounts of (often fuzzy) information while being extremely dense, error-resilient and power efficient.
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Dates and versions

lirmm-03363877 , version 1 (04-10-2021)

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  • HAL Id : lirmm-03363877 , version 1

Cite

Aida Todri-Sanial. Energy Efficient Neuromorphic Computing with Oscillatory Neural Networks. E-MRS 2021 - Fall Meeting of the European Materials Research Society, Dec 2021, Boston, MA, United States. ⟨lirmm-03363877⟩
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