MemTorch: an open-source simulation framework for memristive deep learning systems

Lammie, Corey, Xiang, Wei, Linares-Barranco, Bernabé, and Rahimi Azghadi, Mostafa (2022) MemTorch: an open-source simulation framework for memristive deep learning systems. Neurocomputing, 485. pp. 124-133.

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Abstract

Memristive devices have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using these Resistive Random-Access Memory(RRAM) devices can be used to efficiently implement various in-memory computing operations, such as Multiply Accumulate (MAC) and unrolled-convolutions, which are used extensively in Deep Neural Network(DNN) and Convolutional Neural Network (CNN). However, memristive devices face concerns of aging and non-idealities, which limit the accuracy, reliability, and robustness of Memristive Deep Learning System(MDLS), that should be considered prior to circuit-level realization. This Original Software Publication(OSP) presents MemTorch, an open-source1 framework for customized large-scale memristive Deep Learning(DL) simulations, with a refined focus on the co-simulation of device non-idealities. MemTorch also facilitates co-modelling of key crossbar peripheral circuitry. MemTorch adopts a modernized software engineering methodology and integrates directly with the well-known PyTorch Machine Learning(ML) library.

Item ID: 72622
Item Type: Article (Research - C1)
ISSN: 1872-8286
Keywords: Memristors, ReRAM, Deep Learning, PyTorch
Copyright Information: © 2022 Published by Elsevier B.V.
Date Deposited: 24 Feb 2022 03:00
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 25%
40 ENGINEERING > 4018 Nanotechnology > 401804 Nanoelectronics @ 50%
40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400908 Microelectronics @ 25%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220404 Computer systems @ 50%
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