Modeling and simulating in-memory memristive deep learning systems: an overview of current efforts

Lammie, Corey, Xiang, Wei, and Rahimiazghadi, Mostafa (2022) Modeling and simulating in-memory memristive deep learning systems: an overview of current efforts. Array, 13. 100116.

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Abstract

Deep Learning (DL) systems have demonstrated unparalleled performance in many challenging engineering applications. As the complexity of these systems inevitably increase, they require increased processing capabilities and consume larger amounts of power, which are not readily available in resource-constrained processors, such as Internet of Things (IoT) edge devices. Memristive In-Memory Computing (IMC) systems for DL, entitled Memristive Deep Learning Systems (MDLSs), that perform the computation and storage of repetitive operations in the same physical location using emerging memory devices, can be used to augment the performance of traditional DL architectures; massively reducing their power consumption and latency. However, memristive devices, such as Resistive Random-Access Memory (RRAM) and Phase-Change Memory (PCM), are difficult and cost-prohibitive to fabricate in small quantities, and are prone to various device non-idealities that must be accounted for. Consequently, the popularity of simulation frameworks, used to simulate MDLS prior to circuit-level realization, is burgeoning. In this paper, we provide a survey of existing simulation frameworks and related tools used to model large-scale MDLS. Moreover, we perform direct performance comparisons of modernized open-source simulation frameworks, and provide insights into future modeling and simulation strategies and approaches. We hope that this treatise is beneficial to the large computers and electrical engineering community, and can help readers better understand available tools and techniques for MDLS development.

Item ID: 71173
Item Type: Article (Research - C1)
ISSN: 2590-0056
Keywords: Device modeling, Circuit simulation, Memristors, In-Memory Computing, Deep Learning
Copyright Information: © 2021 The Authors. Published by Elsevier Inc.
Funders: James Cook University (JCU)
Projects and Grants: JCU DRTPS, JCU Rising Start ECR Fellowship
Date Deposited: 10 Jan 2022 23:23
FoR Codes: 40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400901 Analog electronics and interfaces @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation @ 50%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50%
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 50%
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