2D memristors could help solve AI's energy problem
New generations of memristors could reliably store information directly within the molecular structures of graphene-like materials. In a new review published in Nanoenergy Advances, Gennady Panin of the Russian Academy of Sciences shows how these atomically thin materials are ideally suited for electrical circuits that mimic the function of our own brains—and could help address the vast power requirements of emerging AI technologies.
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