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ReRAM Machine Learning Embraces Variability

www.eetimes.com, Mar. 30, 2021 – 

TORONTO–Sometimes a problem can become its own solution.

For CEA-Leti scientists, it means that traits of resistive-RAM (ReRAM) devices that have been previously considered as "non-ideal" may be the answer to overcoming barriers to developing ReRAM-based edge-learning systems, as outlined in a recent Nature Electronics publication titled "In-situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling." It describes how RRAM, or memristor, technology can be used to create intelligent systems that learn locally at the edge, independent of the cloud.

Thomas Dalgaty, a CEA-Leti scientist at France's Université Grenoble, explained how the team were able to navigate the intrinsic non-idealities of ReRAM technology–the learning algorithms used in current ReRAM-based edge approaches cannot be reconciled with device programming randomness, or variability, among others. In a telephone interview with EE Times, he said the solution was to implement a Markov Chain Monte Carlo (MCMC) sampling learning algorithm in a fabricated chip that acts as a Bayesian machine-learning model, which actively exploited memristor randomness.

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