{"id":49639,"date":"2024-07-29T10:39:45","date_gmt":"2024-07-29T09:39:45","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=49639"},"modified":"2024-07-29T10:39:45","modified_gmt":"2024-07-29T09:39:45","slug":"university-of-minnesota-device-slashes-ai-energy-consumption","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/university-of-minnesota-device-slashes-ai-energy-consumption\/49639\/","title":{"rendered":"University of Minnesota device slashes AI energy consumption"},"content":{"rendered":"

Researchers at the University of Minnesota Twin Cities have developed a cutting-edge hardware device that could dramatically reduce AI energy consumption by a factor of at least 1,000.<\/h2>\n

This breakthrough represents a significant leap forward in the quest for more energy-efficient AI applications<\/a>.<\/p>\n

Addressing the energy demands of AI<\/h3>\n

With AI applications increasingly prevalent, there is a pressing need to enhance energy efficiency without compromising performance or escalating costs.<\/p>\n

Traditional AI processes consume vast amounts of power by constantly transferring data between logic (processing) and memory (storage).<\/p>\n

The University of Minnesota’s new model, called computational random-access memory (CRAM)<\/a>, addresses this issue by keeping data within the memory for processing.<\/p>\n

“This work is the first experimental demonstration of CRAM, where data can be processed entirely within the memory array without needing to leave the grid where a computer stores information,” explained Yang Lv, a postdoctoral researcher in the Department of Electrical and Computer Engineering and lead author of the study.<\/p>\n

CRAM: A game-changer in AI energy efficiency<\/h3>\n

The International Energy Agency (IEA) predicts that AI energy consumption will double from 460 terawatt-hours (TWh) in 2022 to 1,000 TWh in 2026<\/a>, comparable to Japan’s total electricity consumption.<\/p>\n

CRAM-based machine learning inference accelerators could achieve energy improvements of up to 1,000 times, with some applications seeing energy savings of 2,500 and 1,700 times compared to traditional methods.<\/p>\n

“Our initial concept to use memory cells directly for computing 20 years ago was considered crazy,” said Jian-Ping Wang, senior author of the paper and a Distinguished McKnight Professor at the University of Minnesota.<\/p>\n

The interdisciplinary team, comprising experts from physics, materials science, computer science, and engineering, has been developing this technology since 2003.<\/p>\n

The research builds on patented work into Magnetic Tunnel Junctions (MTJs), nanostructured devices used in hard drives, sensors, and other microelectronics systems, including Magnetic Random Access Memory (MRAM).<\/p>\n

CRAM leverages these advancements to perform computations directly within memory cells, eliminating slow and energy-intensive data transfers typical of traditional architectures.<\/p>\n

Breaking the von Neumann bottleneck<\/h3>\n

CRAM architecture overcomes the bottleneck of the traditional von Neumann architecture, where computation and memory are separate entities.<\/p>\n

“CRAM is very flexible; computation can be performed in any location in the memory array,” said Ulya Karpuzcu, an Associate Professor and expert on computing architecture.<\/p>\n

This flexibility allows CRAM to match the performance needs of various AI algorithms more efficiently than traditional systems.<\/p>\n

CRAM uses significantly less energy than current random access memory (RAM) devices, which rely on multiple transistors to store data.<\/p>\n

By employing MTJs\u2014a type of spintronic device that uses electron spin instead of electrical charge\u2014CRAM provides a more efficient alternative to traditional transistor-based chips.<\/p>\n

The University of Minnesota team is now collaborating with semiconductor industry leaders to scale up demonstrations and produce the hardware necessary to reduce AI energy consumption on a larger scale.<\/p>\n

The development of CRAM technology represents a monumental step towards sustainable AI computing.<\/p>\n

By dramatically reducing AI energy consumption while maintaining high performance, this innovation promises to meet the growing demands of AI applications and pave the way for a more efficient and environmentally friendly future.<\/p>\n","protected":false},"excerpt":{"rendered":"

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