Accelerating machine learning with Non-Volatile Memory: Exploring device and circuit tradeoffs

Large arrays of the same nonvolatile memories (NVM) being developed for Storage-Class Memory (SCM) - such as Phase Change Memory (PCM) and Resistance RAM (ReRAM) - can also be used in non-Von Neumann neuromorphic computational schemes, with device conductance serving as synaptic "weight."...

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Bibliographic Details
Published in2016 IEEE International Conference on Rebooting Computing (ICRC) pp. 1 - 8
Main Authors Fumarola, Alessandro, Narayanan, Pritish, Sanches, Lucas L., Sidler, Severin, Junwoo Jang, Kibong Moon, Shelby, Robert M., Hyunsang Hwang, Burr, Geoffrey W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
Neural networks
Neurons
Nonvolatile memory
Performance evaluation
Phase change materials
Phase change memory
Training
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Summary:Large arrays of the same nonvolatile memories (NVM) being developed for Storage-Class Memory (SCM) - such as Phase Change Memory (PCM) and Resistance RAM (ReRAM) - can also be used in non-Von Neumann neuromorphic computational schemes, with device conductance serving as synaptic "weight." This allows the all-important multiply-accumulate operation within these algorithms to be performed efficiently at the weight data.
DOI:10.1109/ICRC.2016.7738684