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End-to-end modeling of variability-aware neural networks based on resistive-switching memory arrays

  • Resistive-switching random access memory (RRAM) is a promising technology that enables advanced applications in the field of in-memory computing (IMC). By operating the memory array in the analogue domain, RRAM-based IMC architectures can dramatically improve the energy efficiency of deep neural networks (DNNs). However, achieving a high inference accuracy is challenged by significant variation of RRAM conductance levels, which can be compensated by (i) advanced programming techniques and (ii) variability-aware training (VAT) algorithms. In both cases, however, detailed knowledge and accurate physics-based statistical models of RRAM are needed to develop programming and VAT methodologies. This work presents an end-to-end approach to the development of highly-accurate IMC circuits with RRAM, encompassing the device modeling, the precise programming algorithm, and the VAT simulations to maximize the DNN classification accuracy in presence of conductance variations.

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Metadaten
Author: Artem Glukhov, Nicola Lepri, Valerio Milo, Andrea Baroni, Cristian Zambelli, Piero Olivo, Eduardo Perez, Christian WengerORCiD, Daniele Ielmini
DOI:https://doi.org/10.1109/VLSI-SoC54400.2022.9939653
Title of the source (English):Proc. 30th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC 2022)
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2022
Tag:HfO2; RRAM; memristive switching; neural network
First Page:1
Last Page:5
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien
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