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.

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