Magnetic tunnel junctions mimic synapse behavior for energy-efficient neuromorphic computing
The rapid development of artificial intelligence (AI) poses challenges to today’s computer technology. Conventional silicon processors are reaching their limits: they consume large amounts of energy, the storage and processing units are not interconnected and data transmission slows down complex applications.
Addressing the staggering power and energy demands of artificial intelligence, engineers at the University of Houston have developed a revolutionary new thin-film material that promises to make AI devices significantly faster while dramatically cutting energy consumption.
As artificial intelligence systems grow larger and more powerful, their energy demands are rising dramatically. But recent research from the University of Massachusetts Amherst published in Nature Communications suggests that advanced AI capabilities may be achievable with dramatically lower energy consumption.
AI can help solve some of the world’s biggest challenges — whether climate change, cancer or national security — U.S. Secretary of Energy Jennifer Granholm emphasized today during her remarks at the AI for Science, Energy and Security session at the NVIDIA AI Summit, in Washington, D.C. Granholm went on…