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Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices

Authors:

Gangotree Chakma,

Nicholas D. Skuda,

Catherine D. Schuman,

James S. Plank,

Mark E. Dean,

Garrett S. RoseAuthors Info & Claims

GLSVLSI '18: Proceedings of the 2018 Great Lakes Symposium on VLSI

Pages 379 - 383

https://doi.org/10.1145/3194554.3194611

Published: 30 May 2018 Publication History

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Abstract

Resource constrained devices are the building blocks of the internet of things (IoT) era. Since the idea behind IoT is to develop an interconnected environment where the devices are tiny enough to operate with limited resources, several control systems have been built to maintain low energy and area consumption while operating as IoT edge devices. Several researchers have begun work on implementing control systems built from resource constrained devices using machine learning. However, there are many ways such devices can achieve lower power consumption and area utilization while maximizing application efficiency. Spiky neuromorphic computing (SNC) is an emerging paradigm that can be leveraged in resource constrained devices for several emerging applications. While delivering the benefits of machine learning, SNC also helps minimize power consumption. For example, low energy memory devices (memristors) are often used to achieve low power operation and also help in reducing system area. In total, we anticipate SNC will provide computational efficiency approaching that of deep learning while using low power, resource constrained devices.

References

[1]

Sherif Amer, Sagarvarma Sayyaparaju, Garrett S. Rose, Karsten Beckmann, and Nathaniel C. Cady. {n. d.}. A Practical Hafnium-Oxide Memristor Model Suitable for Circuit Design and Simulation Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS).

Google Scholar

[2]

Karsten Beckmann, Josh Holt, Harika Manem, Joseph Van Nostrand, and Nathaniel C Cady. 2016. Nanoscale Hafnium Oxide RRAM Devices Exhibit Pulse Dependent Behavior and Multi-level Resistance Capability. MRS Advances (2016), 1--6.

Google Scholar

[3]

Nathaniel Cady, Karsten Beckmann, Wilkie Olin-Ammentorp, Gangotree Chakma, Sherif Amer, Ryan Weiss, Sagarvarma Sayyaparaju, Md. Musabbir Adnan, John Murray, Mark Dean, James Plank, Garrett S. Rose, and Joseph E. Van Nostrand. 2018. Full CMOS-Memristor Implementation of a Dynamic Neuromorphic Architecture Proceedings of the Government Microcircuit Applications and Critical Technology Conference (GOMACTech).

Google Scholar

[4]

Gangotree Chakma, Md. Musabbir Adnan, Austin R. Wyer, Ryan Weiss, Catherine D. Schuman, and Garrett S. Rose. 2017 a. Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level. IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) Vol. PP, 99 (2017).

Google Scholar

[5]

G. Chakma, S. Sayyaparaju, R. Weiss, and G. S. Rose. 2017 b. A Mixed-Signal Approach to Memristive Neuromorphic System Design 60th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS). Boston, MA.

Google Scholar

[6]

L. O. Chua. 1971. Memristor-The missing circuit element. IEEE Transactions on Circuit Theory Vol. 18, 5 (September. 1971), 507--519.

Crossref

Google Scholar

[7]

Miao Hu, Hai Li, Yiran Chen, Qing Wu, Garrett S. Rose, and Richard W. Linderman. 2014. Memristor Crossbar-Based Neuromorphic Computing System: A Case Study. IEEE Trans. Neural Netw. Learning Syst. Vol. 25, 10 (October. 2014), 1864--1878.

Crossref

Google Scholar

[8]

Giacomo Indiveri, Bernabé Linares-Barranco, Robert Legenstein, George Deligeorgis, and Themistoklis Prodromakis. 2013. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology Vol. 24, 38 (2013), 384010.

Crossref

Google Scholar

[9]

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE Vol. 86, 11 (November. 1998), 2278--2324.

Crossref

Google Scholar

[10]

HY Lee, YS Chen, PS Chen, TY Wu, F Chen, CC Wang, PJ Tzeng, M-J Tsai, and C Lien. 2010. Low-power and nanosecond switching in robust hafnium oxide resistive memory with a thin Ti cap. IEEE Electron Device Letters Vol. 31, 1 (2010), 44--46.

Crossref

Google Scholar

[11]

Sam Leroux, Steven Bohez, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, and Bart Dhoedt. 2015. Resource-constrained classification using a cascade of neural network layers Neural Networks (IJCNN), 2015 International Joint Conference on. IEEE, 1--7.

Google Scholar

[12]

H. Li, K. Ota, and M. Dong. 2018. Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing. IEEE Network Vol. 32, 1 (Jan. 2018), 96--101.

Crossref

Google Scholar

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Zhang SGalli G(2024) Metallic Interface between Two Insulating Phases of La 1– x Sr x CoO 3−δ Chemistry of Materials10.1021/acs.chemmater.3c0323136:4(2096-2105)Online publication date: 13-Feb-2024
https://doi.org/10.1021/acs.chemmater.3c03231
Firoozi AFiroozi AFiroozi AFiroozi A(2024)Emerging Circuits and Memory TechnologiesRevolutionizing Civil Engineering with Neuromorphic Computing10.1007/978-3-031-71097-1_4(31-38)Online publication date: 13-Sep-2024
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Ramesh A. Sivakumar PVenugopal EElngar A(2023)Energy and Performance Analysis of Robotic Applications Using Artificial Neural NetworkNeuromorphic Computing Systems for Industry 4.010.4018/978-1-6684-6596-7.ch006(144-171)Online publication date: 16-Jun-2023
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Index Terms

Energy and Area Efficiency in Neuromorphic Computing for Resource Constrained Devices
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
  2. Embedded and cyber-physical systems
    1. Embedded systems
      1. Embedded hardware

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Information & Contributors

Information

Published In

GLSVLSI '18: Proceedings of the 2018 Great Lakes Symposium on VLSI

May 2018

533 pages

ISBN:9781450357241

DOI:10.1145/3194554

General Chair:
Deming Chen
University of Illinois, USA
,
Program Chairs:
Houman Homayoun
George Mason University, USA
,
Baris Taskin
Drexel University, USA

© 2018 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 May 2018

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GLSVLSI '18

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SIGDA

GLSVLSI '18: Great Lakes Symposium on VLSI 2018

May 23 - 25, 2018

IL, Chicago, USA

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GLSVLSI '18 Paper Acceptance Rate 48 of 197 submissions, 24%;

Overall Acceptance Rate 312 of 1,156 submissions, 27%

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GLSVLSI '25

Sponsor:
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Great Lakes Symposium on VLSI 2025

June 30 - July 2, 2025

New Orleans , LA , USA

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Cited By

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Zhang SGalli G(2024) Metallic Interface between Two Insulating Phases of La 1– x Sr x CoO 3−δ Chemistry of Materials10.1021/acs.chemmater.3c0323136:4(2096-2105)Online publication date: 13-Feb-2024
https://doi.org/10.1021/acs.chemmater.3c03231
Firoozi AFiroozi AFiroozi AFiroozi A(2024)Emerging Circuits and Memory TechnologiesRevolutionizing Civil Engineering with Neuromorphic Computing10.1007/978-3-031-71097-1_4(31-38)Online publication date: 13-Sep-2024
https://doi.org/10.1007/978-3-031-71097-1_4
Ramesh A. Sivakumar PVenugopal EElngar A(2023)Energy and Performance Analysis of Robotic Applications Using Artificial Neural NetworkNeuromorphic Computing Systems for Industry 4.010.4018/978-1-6684-6596-7.ch006(144-171)Online publication date: 16-Jun-2023
https://doi.org/10.4018/978-1-6684-6596-7.ch006
Singh Yadav RSadashiva AHolla AMuduli PBhowmik D(2023)Impact of edge defects on the synaptic characteristic of a ferromagnetic domain-wall device and on on-chip learningNeuromorphic Computing and Engineering10.1088/2634-4386/acf0e43:3(034006)Online publication date: 25-Aug-2023
https://doi.org/10.1088/2634-4386/acf0e4
Date PKulkarni SYoung ASchuman CPotok TVetter J(2023)Encoding integers and rationals on neuromorphic computers using virtual neuronScientific Reports10.1038/s41598-023-35005-x13:1Online publication date: 6-Jul-2023
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Yadav RGupta PHolla AAli Khan KMuduli PBhowmik D(2023)Demonstration of Synaptic Behavior in a Heavy-Metal-Ferromagnetic-Metal-Oxide-Heterostructure-Based Spintronic Device for On-Chip Learning in Crossbar-Array-Based Neural NetworksACS Applied Electronic Materials10.1021/acsaelm.2c014885:1(484-497)Online publication date: 13-Jan-2023
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Singh YSingh A(2022)Retracted: Minimizing Memory Usage for Resource Constrained Devices using Deep Convolutional Neural Networks2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon)10.1109/MysuruCon55714.2022.9972384(1-6)Online publication date: 16-Oct-2022
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Schuman CPlank JRose G(2022)Application-Hardware Co-Design: System-Level Optimization of Neuromorphic Computers with Neuromorphic Devices2022 International Electron Devices Meeting (IEDM)10.1109/IEDM45625.2022.10019362(2.4.1-2.4.4)Online publication date: 3-Dec-2022
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Date PKulkarni SYoung ASchuman CPotok TVetter J(2022)Virtual Neuron: A Neuromorphic Approach for Encoding Numbers2022 IEEE International Conference on Rebooting Computing (ICRC)10.1109/ICRC57508.2022.00017(100-105)Online publication date: Dec-2022
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Parker LChabot EDicecco JKoziol S(2022)Implementation and Feedback Control Tuning of an Analog Izhikevich Neuron CircuitIEEE Access10.1109/ACCESS.2022.318471910(67289-67304)Online publication date: 2022
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