Artificial neural networks could soon process time-dependent data more efficiently with the development of a tunable memristor. This technology, detailed in a University of Michigan-led study, could significantly reduce AI energy consumption. Credit: SciTechDaily.com
“Right now, there’s a lot of interest in AI, but to process bigger and more interesting data, the approach is to increase the network size. That’s not very efficient,” said Wei Lu, the James R. Mellor Professor of Engineering at U-M and co-corresponding author of the study with John Heron, U-M associate professor of materials science and engineering.
Heron calls this type of oxide, an entropy-stabilized oxide, the “kitchen sink of the atomic world”—the more elements they add, the more stable it becomes. By changing the ratios of these oxides, the team achieved time constants ranging from 159 to 278 nanoseconds, or trillionths of a second. The simple memristor network they built learned to recognize the sounds of the numbers zero to nine. Once trained, it could identify each number before the audio input was complete.
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