A team of researchers at MIT are working on hardware for artificial intelligence that offers faster computing with less power. The analog deep learning technique involves sending protons through solids at extremely fast speeds. “The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.
Find the technical paper “Nanosecond protonic programmable resistors for analog deep learning” here. Published July 2022.
SCIENCE
28 Jul 2022
Vol 377, Issue 6605
pp. 539-543
DOI: 10.1126/science.abp8064
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