MemTorch: a simulation framework for deep memristive cross-bar architectures

Lammie, Corey, and Rahimi Azghadi, Mostafa (2020) MemTorch: a simulation framework for deep memristive cross-bar architectures. In: Proceedings of the 2020 IEEE International Symposium on Circuits and Systems. From: ISCAS: 2020 IEEE International Symposium on Circuits and Systems, 10-21 October 2020, Seville, Spain.

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Abstract

Memristive devices arranged in cross-bar architectures have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems for deployment in resource-constrained platforms, such as the Internet-of-Things (IoT) edge devices. These cross-bar architectures can be used to implement various in-memory computing operations, such as Multiply-Accumulate (MAC) and convolution, which are used extensively in Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). Currently, there is a lack of an open source, general, high-level simulation platform that can fully integrate any behavioral or experimental memristive device model into cross-bar architectures. This paper presents such a framework named MemTorch, which integrates directly with the well-known PyTorch Machine Learning (ML) library. To demonstrate an example practical use of MemTorch, we use it to simulate the performance degradation that non-ideal devices introduce to a typical Memristive DNN (MDNN) implementing VGG-16 for CIFAR-10. Our open source 1 MemTorch framework can be used by circuit and system designers to conveniently build customized large-scale simulation platforms, as a preliminary step before circuit-level realization.

Item ID: 65711
Item Type: Conference Item (Research - E1)
ISBN: 978-1-7281-3320-1
Copyright Information: (C) IEEE
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Date Deposited: 04 Feb 2021 01:03
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4612 Software engineering > 461299 Software engineering not elsewhere classified @ 50%
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