research-article

Memristors for neural branch prediction: a case study in strict latency and write endurance challenges

Authors:

Hebatallah Saadeldeen,

Diana Franklin,

Charlotte Hill,

Dmitri Strukov,

Timothy Sherwood,

Frederic T. ChongAuthors Info & Claims

CF '13: Proceedings of the ACM International Conference on Computing Frontiers

Article No.: 26, Pages 1 - 10

https://doi.org/10.1145/2482767.2482801

Published: 14 May 2013 Publication History

Abstract

Memristors offer many potential advantages over more traditional memory-cell technologies, including the potential for extreme densities, and fast read times. Current devices, however, are plagued by problems of yield, and durability. We present a limit study of an aggressive neural network application that has a high update rate and a strict latency requirement, analog neural branch predictor. Of course, traditional analog neural network (ANN) implementations of branch predictors are not built with the idea that the underlying bits are likely to fail due to both manufacturing and wear-out issues. Without some careful precautions, a direct one-to-one replacement will result in poor behavior.

We propose a hybrid system that uses SRAM front-end cache, and a distributed-sum scheme to overcome memristors' limitations. Our design can leverage devices with even modest durability (surviving only hours of continuous switching) to provide a system lasting 5 or more years of continuous operation. In addition, these schemes allow for a fault-tolerant design as well. We find that, while a neural predictor benefits from larger density, current technology parameters do not allow high dense, energy-efficient design. Thus, we discuss a range of plausible memristor characteristics that would; as the technology advances; make them practical for our application.

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Cited By

Jalali ZWang CKearney GYuan GDing CZhou YWang YSoundarajan S(2023)Memristor-Based Spectral Decomposition of Matrices and Its ApplicationsIEEE Transactions on Computers10.1109/TC.2022.320274672:5(1460-1472)Online publication date: 1-May-2023
https://doi.org/10.1109/TC.2022.3202746
Mayacela MRentería LContreras LMedina S(2022)Comparative Analysis of Reconfigurable Platforms for Memristor EmulationMaterials10.3390/ma1513448715:13(4487)Online publication date: 25-Jun-2022
https://doi.org/10.3390/ma15134487
Poduval PNi YKim YNi KKumar RCammarota RImani MOshana R(2022)Adaptive neural recovery for highly robust brain-like representationProceedings of the 59th ACM/IEEE Design Automation Conference10.1145/3489517.3530659(367-372)Online publication date: 10-Jul-2022
https://dl.acm.org/doi/10.1145/3489517.3530659
Show More Cited By

Index Terms

Memristors for neural branch prediction: a case study in strict latency and write endurance challenges
1. Hardware
  1. Integrated circuits
    1. Semiconductor memory

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Published In

cover image ACM Conferences

CF '13: Proceedings of the ACM International Conference on Computing Frontiers

May 2013

302 pages

ISBN:9781450320535

DOI:10.1145/2482767

General Chairs:
Hubertus Franke
IBM, US
,
Alexander Heinecke
TU München, DE
,
Program Chairs:
Krishna Palem
Rice University, US and Nanyang Technological University, SG
,
Eli Upfal
Brown University

Copyright © 2013 ACM.

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SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 May 2013

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Division of Computing and Communication Foundations

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CF'13

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SIGMICRO

CF'13: Computing Frontiers Conference

May 14 - 16, 2013

Ischia, Italy

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CF '13 Paper Acceptance Rate 26 of 49 submissions, 53%;

Overall Acceptance Rate 273 of 785 submissions, 35%

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22nd ACM International Conference on Computing Frontiers

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Cited By

Jalali ZWang CKearney GYuan GDing CZhou YWang YSoundarajan S(2023)Memristor-Based Spectral Decomposition of Matrices and Its ApplicationsIEEE Transactions on Computers10.1109/TC.2022.320274672:5(1460-1472)Online publication date: 1-May-2023
https://doi.org/10.1109/TC.2022.3202746
Mayacela MRentería LContreras LMedina S(2022)Comparative Analysis of Reconfigurable Platforms for Memristor EmulationMaterials10.3390/ma1513448715:13(4487)Online publication date: 25-Jun-2022
https://doi.org/10.3390/ma15134487
Poduval PNi YKim YNi KKumar RCammarota RImani MOshana R(2022)Adaptive neural recovery for highly robust brain-like representationProceedings of the 59th ACM/IEEE Design Automation Conference10.1145/3489517.3530659(367-372)Online publication date: 10-Jul-2022
https://dl.acm.org/doi/10.1145/3489517.3530659
Liao ZFu JWang J(2021)Ameliorate Performance of Memristor-Based ANNs in Edge ComputingIEEE Transactions on Computers10.1109/TC.2021.308198570:8(1299-1310)Online publication date: 1-Aug-2021
https://doi.org/10.1109/TC.2021.3081985
Xiong ABai WLong LJiang Y(2020)Non-volatile Memory Page Allocation Mechanism2020 6th International Conference on Control, Automation and Robotics (ICCAR)10.1109/ICCAR49639.2020.9108088(21-26)Online publication date: Apr-2020
https://doi.org/10.1109/ICCAR49639.2020.9108088
Jokar MZhang LChong FJacob B(2018)Cooperative NV-NUMAProceedings of the International Symposium on Memory Systems10.1145/3240302.3240308(67-78)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1145/3240302.3240308
Philipp GStephan BKai-Uwe S(2018)An NVM-Aware Storage Layout for Analytical Workloads2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW.2018.00025(110-115)Online publication date: Apr-2018
https://doi.org/10.1109/ICDEW.2018.00025
Mittal S(2018)A survey of techniques for dynamic branch predictionConcurrency and Computation: Practice and Experience10.1002/cpe.466631:1Online publication date: 2-Sep-2018
https://doi.org/10.1002/cpe.4666
Deng ZFeldman AKurtz SChong F(2017)Lemonade from LemonsACM SIGARCH Computer Architecture News10.1145/3140659.308022645:2(361-374)Online publication date: 24-Jun-2017
https://dl.acm.org/doi/10.1145/3140659.3080226
Saadeldeen HDeng ZSherwood TChong FJacob B(2017)Thermal-aware, heterogeneous materials for improved energy and reliability in 3D PCM architecturesProceedings of the International Symposium on Memory Systems10.1145/3132402.3132407(223-236)Online publication date: 2-Oct-2017
https://dl.acm.org/doi/10.1145/3132402.3132407
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