CEA-Leti Scientists Demonstrate Machine Learning Technique Exploiting RRAM Devices

By Perry Cohen

Associate Editor

Embedded Computing Design

January 19, 2021

News

CEA-Leti Scientists Demonstrate Machine Learning Technique Exploiting RRAM Devices

CEA-Leti announced that its scientists have demonstrated a machine-learning technique exploiting what used to be considered non-ideal traits of RRAM devices. This overcomes barriers in the development of RRAM-based edge-learning systems.

CEA-Leti announced that its scientists have demonstrated a machine-learning technique exploiting what used to be considered non-ideal traits of RRAM devices. This overcomes barriers in the development of RRAM-based edge-learning systems.

In the January edition of “Nature Electronics,” the research team at CEA-Leti portrayed how RRAM, or memristor technology is able to be used to create intelligent systems that learn locally at the edge. The algorithm, which is sued in current RRAM-based edge approaches, can’t be reconciled with device programming randomness or variability.

In order to subside the problem, the team developed a method that actively exploits that memristor randomness, implementing a Markov Chain Monte Carlo (MCMC) sampling learning algorithm in a fabricated chip that acts as a Bayesian machine-learning model.

According to a company press release, while machine learning provides the enabling models and algorithms for edge-learning systems, increased attention concerning how these algorithms map onto hardware is required to bring machine learning to the edge. Machine-learning models are normally trained using general purpose hardware based on a von Neumann architecture, which is unsuited for edge learning because of the energy required to continuously move information between separated processing and memory centers on-chip.

RRAM technology has been applied to in-memory implementations of backpropagation algorithms to implement in-situ learning on edge systems.

For more information, visit http://www.leti-cea.com/.

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Perry Cohen, associate editor for Embedded Computing Design, is responsible for web content editing and creation, podcast production, and social media efforts. Perry has been published on both local and national news platforms including KTAR.com (Phoenix), ArizonaSports.com (Phoenix), AZFamily.com, Cronkite News, and MLB/MiLB among others. Perry received a BA in Journalism from the Walter Cronkite School of Journalism and Mass Communications at Arizona State university.

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