research-article

Statistical memristor modeling and case study in neuromorphic computing

Authors:

Robinson E. Pino,

Hai (Helen) Li,

Beiye LiuAuthors Info & Claims

DAC '12: Proceedings of the 49th Annual Design Automation Conference

Pages 585 - 590

https://doi.org/10.1145/2228360.2228466

Published: 03 June 2012 Publication History

Abstract

Memristor, the fourth passive circuit element, has attracted increased attention since it was rediscovered by HP Lab in 2008. Its distinctive characteristic to record the historic profile of the voltage/current creates a great potential for future neuromorphic computing system design. However, at the nano-scale, process variation control in the manufacturing of memristor devices is very difficult. The impact of process variations on a memristive system that relies on the continuous (analog) states of the memristors could be significant. We use TiO₂-based memristor as an example to analyze the impact of geometry variations on the electrical properties. A simple algorithm was proposed to generate a large volume of geometry variation-aware three-dimensional device structures for Monte-Carlo simulations. A neuromorphic computing system based on memristor-based bidirectional synapse design is proposed as case study. We analyze and evaluate the robustness of the proposed system in pattern recognition based on massive Monte-Carlo simulations, after considering input defects and process variations.

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Cited By

Siddique MZhang YAn H(2023)Monitoring time domain characteristics of Parkinson’s disease using 3D memristive neuromorphic systemFrontiers in Computational Neuroscience10.3389/fncom.2023.127457517Online publication date: 15-Dec-2023
https://doi.org/10.3389/fncom.2023.1274575
Amirsoleimani ALiu TAlibart FEccofey SChang YDrouin DGenov R(2021)Mitigating State-Drift in Memristor Crossbar Arrays for Vector Matrix MultiplicationMemristor - An Emerging Device for Post-Moore’s Computing and Applications [Working Title]10.5772/intechopen.100246Online publication date: 24-Sep-2021
https://doi.org/10.5772/intechopen.100246
Khalil NHezayyin HSaid LMadian ARadwan A(2021)Active emulation circuits of fractional-order memristive elements and its applicationsAEU - International Journal of Electronics and Communications10.1016/j.aeue.2021.153855138(153855)Online publication date: Aug-2021
https://doi.org/10.1016/j.aeue.2021.153855
Show More Cited By

Index Terms

Statistical memristor modeling and case study in neuromorphic computing
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

DAC '12: Proceedings of the 49th Annual Design Automation Conference

June 2012

1357 pages

ISBN:9781450311991

DOI:10.1145/2228360

General Chair:
Patrick Groeneveld
Magma Design Automation, Inc., San Jose, CA
,
Program Chairs:
Donatella Sciuto
Politecnico di Milano, Milano, Italy
,
Soha Hassoun
Tufts Univ., Medford, MA

Copyright © 2012 ACM.

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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EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
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SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 June 2012

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DAC '12: The 49th Annual Design Automation Conference 2012

June 3 - 7, 2012

California, San Francisco

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Siddique MZhang YAn H(2023)Monitoring time domain characteristics of Parkinson’s disease using 3D memristive neuromorphic systemFrontiers in Computational Neuroscience10.3389/fncom.2023.127457517Online publication date: 15-Dec-2023
https://doi.org/10.3389/fncom.2023.1274575
Amirsoleimani ALiu TAlibart FEccofey SChang YDrouin DGenov R(2021)Mitigating State-Drift in Memristor Crossbar Arrays for Vector Matrix MultiplicationMemristor - An Emerging Device for Post-Moore’s Computing and Applications [Working Title]10.5772/intechopen.100246Online publication date: 24-Sep-2021
https://doi.org/10.5772/intechopen.100246
Khalil NHezayyin HSaid LMadian ARadwan A(2021)Active emulation circuits of fractional-order memristive elements and its applicationsAEU - International Journal of Electronics and Communications10.1016/j.aeue.2021.153855138(153855)Online publication date: Aug-2021
https://doi.org/10.1016/j.aeue.2021.153855
Diao LSi GZhu JDing Z(2016)A polynomial fractional-order charge-controlled memristor model2016 12th World Congress on Intelligent Control and Automation (WCICA)10.1109/WCICA.2016.7578542(900-904)Online publication date: Jun-2016
https://doi.org/10.1109/WCICA.2016.7578542
Liu BYang CLi HChen YWu QBarnell M(2016)Security of neuromorphic systems: Challenges and solutions2016 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS.2016.7527493(1326-1329)Online publication date: May-2016
https://doi.org/10.1109/ISCAS.2016.7527493
Cui JQiu Q(2016)Towards memristor based accelerator for sparse matrix vector multiplication2016 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS.2016.7527185(121-124)Online publication date: May-2016
https://doi.org/10.1109/ISCAS.2016.7527185
Si GDiao LZhu J(2016)Fractional-order charge-controlled memristor: theoretical analysis and simulationNonlinear Dynamics10.1007/s11071-016-3215-187:4(2625-2634)Online publication date: 30-Nov-2016
https://doi.org/10.1007/s11071-016-3215-1
Rajendran JKarri RRose G(2015)Improving Tolerance to Variations in Memristor-Based Applications Using Parallel MemristorsIEEE Transactions on Computers10.1109/TC.2014.230818964:3(733-746)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1109/TC.2014.2308189
Liu BChen YWysocki BHuang T(2015)Reconfigurable Neuromorphic Computing System with Memristor-Based Synapse DesignNeural Processing Letters10.1007/s11063-013-9315-841:2(159-167)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1007/s11063-013-9315-8
Li BWang YChen YLi HYang HFettweis GNebel W(2014)ICEProceedings of the conference on Design, Automation & Test in Europe10.5555/2616606.2616832(1-4)Online publication date: 24-Mar-2014
https://dl.acm.org/doi/10.5555/2616606.2616832
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