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A System-Level Simulator for RRAM-Based Neuromorphic Computing Chips

Published: 08 January 2019 Publication History
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  • Abstract

    Advances in non-volatile resistive switching random access memory (RRAM) have made it a promising memory technology with potential applications in low-power and embedded in-memory computing devices owing to a number of advantages such as low-energy consumption, low area cost and good scaling. There have been proposals to employ RRAM in architecting chips for neuromorphic computing and artificial neural networks where matrix-vector multiplication can be computed in the analog domain in a single timestep. However, it is challenging to employ RRAM devices in neuromorphic chips owing to the non-ideal behavior of RRAM. In this article, we propose a cycle-accurate and scalable system-level simulator that can be used to study the effects of using RRAM devices in neuromorphic computing chips. The simulator models a spatial neuromorphic chip architecture containing many neural cores with RRAM crossbars connected via a Network-on-Chip (NoC). We focus on system-level simulation and demonstrate the effectiveness of our simulator in understanding how non-linear RRAM effects such as stuck-at-faults (SAFs), write variability, and random telegraph noise (RTN) can impact an application’s behavior. By using our simulator, we show that RTN and write variability can have adverse effects on an application. Nevertheless, we show that these effects can be mitigated through proper design choices and the implementation of a write-verify scheme.

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    Published In

    cover image ACM Transactions on Architecture and Code Optimization
    ACM Transactions on Architecture and Code Optimization  Volume 15, Issue 4
    December 2018
    706 pages
    ISSN:1544-3566
    EISSN:1544-3973
    DOI:10.1145/3284745
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 08 January 2019
    Accepted: 01 October 2018
    Revised: 01 September 2018
    Received: 01 May 2018
    Published in TACO Volume 15, Issue 4

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    Author Tags

    1. Neuromorphic computing
    2. RRAM
    3. simulator

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    • Singapore Government Research, Innovation and Enterprise 2020 plan
    • Advanced Manufacturing and Engineering domain

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    • (2024)From Oxides to 2D Materials: Advancing Memristor Technologies for Energy-Efficient Neuromorphic ComputingACS Applied Electronic Materials10.1021/acsaelm.4c004286:6(3998-4015)Online publication date: 30-May-2024
    • (2023)Simeuro: A Hybrid CPU-GPU Parallel Simulator for Neuromorphic Computing ChipsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.329179534:10(2767-2782)Online publication date: Oct-2023
    • (2023)Impact of Non-Volatile Memory Cells on Spiking Neural Network Annealing Machine With In-Situ Synapse ProcessingIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2023.330501070:11(4380-4393)Online publication date: Nov-2023
    • (2023)Multi-Objective Architecture Search and Optimization for Heterogeneous Neuromorphic Architecture2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323779(1-8)Online publication date: 28-Oct-2023
    • (2023)SpikeNC: An Accurate and Scalable Simulator for Spiking Neural Network on Multi-Core Neuromorphic Hardware2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC)10.1109/HiPC58850.2023.00052(357-366)Online publication date: 18-Dec-2023
    • (2023)ANAS: Asynchronous Neuromorphic Hardware Architecture Search Based on a System-Level Simulator2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10247850(1-6)Online publication date: 9-Jul-2023
    • (2023)Recent Advances and Future Prospects for Memristive Materials, Devices, and SystemsACS Nano10.1021/acsnano.3c0350517:13(11994-12039)Online publication date: 29-Jun-2023
    • (2023)Ultra-low power resistive random-access memory based on VO2/TiO2 nanotubes composite filmVacuum10.1016/j.vacuum.2023.112472216(112472)Online publication date: Oct-2023
    • (2022)Hierarchical Network Connectivity and Partitioning for Reconfigurable Large-Scale Neuromorphic SystemsFrontiers in Neuroscience10.3389/fnins.2021.79765415Online publication date: 31-Jan-2022
    • (2022)MNEMOSENE: Tile Architecture and Simulator for Memristor-based Computation-in-memoryACM Journal on Emerging Technologies in Computing Systems10.1145/348582418:3(1-24)Online publication date: 29-Jan-2022
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