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Technology-Aware Drift Resilience Analysis of RRAM Crossbar Array Configurations

  • In-memory computing with resistive-switching random access memory (RRAM) crossbar arrays has the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. However, RRAM devices are subject to several non-idealities that result in significant inference accuracy drops compared with software baseline accuracy. A critical one is related to the drift of the conductance states appearing immediately at the end of program and verify algorithms that are mandatory for accurate multi-level conductance operation. The support of drift models in state-of-the-art simulation tools of memristive computationin-memory is currently only in the early stage, since they overlook key device- and array-level parameters affecting drift resilience such as the programming algorithm of RRAM cells, the choice of target conductance states and the weight-toconductance mapping scheme. The goal of this paper is to fully expose these parameters to RRAM crossbar designers as a multi-dimensionalIn-memory computing with resistive-switching random access memory (RRAM) crossbar arrays has the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. However, RRAM devices are subject to several non-idealities that result in significant inference accuracy drops compared with software baseline accuracy. A critical one is related to the drift of the conductance states appearing immediately at the end of program and verify algorithms that are mandatory for accurate multi-level conductance operation. The support of drift models in state-of-the-art simulation tools of memristive computationin-memory is currently only in the early stage, since they overlook key device- and array-level parameters affecting drift resilience such as the programming algorithm of RRAM cells, the choice of target conductance states and the weight-toconductance mapping scheme. The goal of this paper is to fully expose these parameters to RRAM crossbar designers as a multi-dimensional optimization space of drift resilience. For this purpose, a simulation framework is developed, which comes with the suitable abstractions to propagate the effects of those RRAM crossbar configuration parameters to their ultimate implications over inference performance stability.show moreshow less

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Metadaten
Author: Daniel Reiser, Marc ReichenbachORCiD, Tommaso Rizzi, Andrea Baroni, Markus Fritscher, Christian WengerORCiD, Cristian Zambelli, Davide Bertozzi
DOI:https://doi.org/10.1109/NEWCAS57931.2023
ISBN:979-8-3503-0024-6
ISBN:979-8-3503-0025-3
Title of the source (English):21st IEEE Interregional NEWCAS Conference (NEWCAS), 26-28 June 2023, Edinburgh, United Kingdom
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference publication peer-reviewed
Language:English
Year of publication:2023
Tag:RRAM
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien
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