Engineering researchers at the University of Minnesota Twin Cities have demonstrated a state-of-the-art hardware device that could reduce energy consumption for artificial intelligent (AI) computing applications by a factor of at least 1,000.

A team of researchers in the University of Minnesota College of Science and Engineering demonstrated a new model where the data never leaves the memory, called computational random-access memory (CRAM).  

“This work is the first experimental demonstration of CRAM, where the data can be processed entirely within the memory array without the need to leave the grid where a computer stores information,” said Yang Lv, a University of Minnesota Department of Electrical and Computer Engineering postdoctoral researcher and first author of the paper.

According to the new paper’s authors, a CRAM-based machine learning inference accelerator is estimated to achieve an improvement on the order of 1,000. Another example showed an energy savings of 2,500 and 1,700 times compared to traditional methods.