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By Tom Abate
Smartwatches and other battery-powered electronics would be even smarter if they could run AI algorithms. But efforts to build AI-capable chips for mobile devices have so far hit a wall – the so-called “memory wall” that separates data processing and memory chips that must work together to meet the massive and continually growing computational demands imposed by AI.
Hardware and software innovations give eight chips the illusion that they’re one mega-chip working together to run AI. (Image credit: Stocksy / Drea Sullivan)
“Transactions between processors and memory can consume 95 percent of the energy needed to do machine learning and AI, and that severely limits battery life,” said computer scientist Subhasish Mitra, senior author of a new study published in ....

Yunfeng Xin , Hs Philip Wong , Robert Radway , Zainabf Khan , Tony Wu , Andrew Bartolo , Paulc Jolly , Inez Kerr Bell , Mary Wootters , Subhasish Mitra , Pulkit Tandon , Stanford Systemx Alliance , Intel Corporation , Nanyang Technological University , Facebook Inc , Nature Electronics , San Jose State University , National Science Foundation , Electronics Resurgence Initiative , School Of Engineering , Semiconductor Research Corporation , Defense Advanced Research Projects Agency , Andrea Sullivan , Philip Wong , Illusion System , Defense Advanced Research Projects ,