Researchers at the University of Tokyo Institute of Industrial Science in Japan stacked resistive random-access memory modules for artificial intelligence (AI) applications in a novel three-dimensional spiral.
The modules feature oxide semiconductor access transistors, which boost the efficiency of the machine learning training process.
The team further enhanced energy efficiency via a system of binarized neural networks, which restricts the parameters to be either +1 or -1, rather than any number, to compress the volume of data to be stored.
In having the device interpret a database of handwritten digits, the researchers learned that increasing the size of each circuit layer could improve algorithmic accuracy to approximately 90%.
The University of Tokyo's Masaharu Kobayashi said, "In order to keep energy consumption low as AI becomes increasingly integrated into daily life, we need more specialized hardware to handle these tasks efficiently."
From University of Tokyo
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