New Devices Might Scale the Memory Wall

The hunt is on for anything that can surmount AI’s perennial memory wall–even quick models are bogged down by the time and energy needed to carry data between processor and memory. Resistive RAM (RRAM)could circumvent the wall by allowing computation to happen in the memory itself. Unfortunately, most types of this nonvolatile memory are too unstable and unwieldy for that purpose.Fortunately, a potential solution may be at hand. At December’s IEEE International Electron Device Meeting (IEDM), researchers from the University of California, San Diego showed they could run a learning algorithm on an entirely new type of RRAM.“We actually redesigned RRAM, completely rethinking the way it switches,” says Duygu Kuzum, an electrical engineer at the University of California, San Diego, who led the work.RRAM stores data as a level of resistance to the flow of current. The key digital operation in a neural network—multiplying arrays of numbers and then summing the results—can be d