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Título: | MemTorch: An open-source simulation framework for memristive deep learning systems |
Autor: | Lammie, Corey; Xiang, Wei; Linares-Barranco, Bernabé CSIC ORCID; Azghadi, Mostafa, R. | Fecha de publicación: | 2022 | Editor: | Elsevier | Citación: | Neurocomputing 485: 124-133 (2022) | Resumen: | 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-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. | Versión del editor: | https://doi.org/10.1016/j.neucom.2022.02.043 | URI: | http://hdl.handle.net/10261/336928 | DOI: | 10.1016/j.neucom.2022.02.043 | E-ISSN: | 1872-8286 |
Aparece en las colecciones: | (IMSE-CNM) Artículos |
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