Satofumi Souma on Muck Rack

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Two Orders of Magnitude Reduction in Computational Load Achieved by Ultrawideband Responses of an Ion-Gating Reservoir

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By Daiki Nishioka, Wataru Namiki, Hina Kitano, Satofumi Souma
| dx.doi.org
The rapid development of machine learning (ML) technologies, represented by deep learning (DL) and generative artificial intelligence (AI), has significantly increased power consumption, creating a serious social challenge despite the tremendous benefits provided. (1,2) This high energy demand renders conventional cloud-based computing systems unsustainable and necessitates a shift to low-power alternatives like edge computing, where information is processed locally.
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