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arXiv · 2004.10971

MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems

Corey Lammie·Wei Xiang·Bernabé Linares-Barranco·Mostafa Rahimi Azghadi
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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 Networks (DNNs) and Convolutional Neural Networks (CNNs). However, memristive devices face concerns of aging and non-idealities, which limit the accuracy, reliability, and robustness of Memristive Deep Learning Systems (MDLSs), that should be considered prior to circuit-level realization. This Original Software Publication (OSP) presents MemTorch, an open-source framework for customized large-scale memristive 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 soft-ware engineering methodology and integrates directly with the well-known PyTorch Machine Learning (ML) library

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Corey Lammie, Wei Xiang, Bernabé Linares-Barranco, Mostafa Rahimi Azghadi. 2020-04-23. MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems. https://doi.org/10.1016/j.neucom.2022.02.043

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