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Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators

  • Resistive-switching random access memory (RRAM) is a promising technology for in-memory computing (IMC) to accelerate training and inference of deep neural networks (DNNs). This work presents the first physics-based statistical model describing (i) multilevel RRAM device program/verify (PV) algorithms by controlled set transition, (ii) the stochastic cycle-to-cycle (C2C) and device-to-device (D2D) variations within the array, and (iii) the impact of such imprecisions on the accuracy of DNN accelerators. The model can handle the full chain from RRAM materials/device parameters to the DNN performance, thus providing a valuable tool for device/circuit codesign of hardware DNN accelerators.

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Metadaten
Author: Artem Glukhov, Valerio Milo, Andrea Baroni, Nicola Lepri, Cristian Zambelli, Piero Olivo, Eduardo Perez, Christian WengerORCiD, Daniele Ielmini
DOI:https://doi.org/10.1109/IRPS48227.2022.9764497
ISBN:978-1-6654-7950-9
ISBN:978-1-6654-7951-6
ISSN:2473-2001
Title of the source (English):2022 IEEE International Reliability Physics Symposium (IRPS)
Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2022
Tag:Multilevel switching; RRAM; neural network
First Page:3C.3-1
Last Page:3C.3-7
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien
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