Experimental Assessment of Multilevel RRAM-based Vector-Matrix Multiplication Operations for In-Memory Computing
- Resistive random access memory (RRAM)-based hardware accelerators are playing an important role in the implementation of in-memory computing (IMC) systems for artificial intelligence applications. The latter heavily rely on vector-matrix multiplication (VMM) operations that can be efficiently boosted by RRAM devices. However, the stochastic nature of the RRAM technology is still challenging real hardware implementations. To study the accuracy degradation of consecutive VMM operations, in this work we programed two RRAM subarrays composed of 8x8 one-transistor-one-resistor (1T1R) cells following two different distributions of conductive levels. We analyze their robustness against 1000 identical consecutive VMM operations and monitor the inherent devices’ nonidealities along the test. We finally quantize the accuracy loss of the operations in the digital domain and consider the trade-offs between linearly distributing the resistive states of the RRAM cells and their robustness against nonidealities for future implementation of IMCResistive random access memory (RRAM)-based hardware accelerators are playing an important role in the implementation of in-memory computing (IMC) systems for artificial intelligence applications. The latter heavily rely on vector-matrix multiplication (VMM) operations that can be efficiently boosted by RRAM devices. However, the stochastic nature of the RRAM technology is still challenging real hardware implementations. To study the accuracy degradation of consecutive VMM operations, in this work we programed two RRAM subarrays composed of 8x8 one-transistor-one-resistor (1T1R) cells following two different distributions of conductive levels. We analyze their robustness against 1000 identical consecutive VMM operations and monitor the inherent devices’ nonidealities along the test. We finally quantize the accuracy loss of the operations in the digital domain and consider the trade-offs between linearly distributing the resistive states of the RRAM cells and their robustness against nonidealities for future implementation of IMC hardware systems.…
Author: | Emilio Perez-Bosch Quesada, Mamathamba Kalishettyhalli Mahadevaiah, Tommaso Rizzi, Jianan Wen, Markus Ulbricht, Milos Krstic, Christian WengerORCiD, Eduardo Perez |
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DOI: | https://doi.org/10.1109/TED.2023.3244509 |
ISSN: | 0018-9383 |
Title of the source (English): | IEEE Transactions on Electron Devices |
Document Type: | Scientific journal article peer-reviewed |
Language: | English |
Year of publication: | 2023 |
Tag: | RRAM; Vector Matrix Multiplication; variability |
Volume/Year: | 70 |
Issue number: | 4 |
First Page: | 2009 |
Last Page: | 2014 |
Fundername (not EU): | BMBF |
Project number (not EU): | 16ES1002, 16FMD01K, 16FMD02, 16FMD03, 16ME0092 |
Faculty/Chair: | Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien |