“The kitchen sink of the atomic world”: Affordable material could pay crucial role in reducing AI energy use by a factor of 90 — memristors may help current AI system mimic biological neural networks function

Researchers at the University of Michigan have developed a memristor with a tunable relaxation time, potentially leading to more efficient artificial neural networks capable of time-dependent information processing.

Published in Nature Electronics, the study highlights the potential of memristors, electronic components that function as memory devices and can retain their resistance state even when the power is turned off. 

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