CEA-Leti Reports Machine-Learning Breakthrough That Opens Way to Edge Learning

Article in Nature Electronics Details Method that Takes Advantage of RRAM Non- Idealities To Create Intelligent Systems that Have Potential Medical-Diagnostic Applications GRENOBLE, France - Jan. CEA-Leti scientists have demonstrated a machine-learning technique exploiting what have been previously considered as "non-ideal" traits of resistive-RAM (RRAM) devices, overcoming barriers to developing RRAM-based edge-learning systems. Reported in a paper published in the January issue of Nature Electronics titled, "In-situ learning using intrinsic memristor variability via Markov chain Monte Carlo sampling", the research team demonstrated how RRAM, or memristor, technology can be used to create intelligent systems that learn locally at the edge, independent of the cloud. The learning algorithms used in current RRAM-based edge approaches cannot be reconciled with device programming randomness, or variability, as well as other intrinsic non-idealities of the technology. To get around that 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. The article notes that 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.