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.
Memristors work by mimicking key aspects of the way artificial and biological neural networks function without external memory. This property could significantly reduce energy needs in AI, which is important as the technology’s electricity consumption is projected to rise significantly in the coming years.
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