More Web Proxy on the site http://driver.im/

research-article

Public Access

Enabling a new era of brain-inspired computing: energy-efficient spiking neural network with ring topology

Authors:

Yang YiAuthors Info & Claims

DAC '18: Proceedings of the 55th Annual Design Automation Conference

Article No.: 166, Pages 1 - 6

https://doi.org/10.1145/3195970.3196044

Published: 24 June 2018 Publication History

Abstract

The reservoir computing, an emerging computing paradigm, has proven its benefit to multifarious applications. In this work, we successfully designed and fabricated an analog delayed feedback reservoir (DFR) chip. Measurement results demonstrate its rich dynamic behaviors and high energy efficiency. System performance, as well as the robustness, are evaluated. The application of video frame recognition is investigated using a hybrid neural network, which employs the multilayer perceptron (MLP) training model as the readout layer of our designed DFR system, and yields 98% classification accuracy. Compared to results of using the MLP training only, our hybrid training model exhibits much higher recognition rate and accuracy.

References

[1]

Modha, D.S., Introducing a brain-inspired computer. Published online at http://www.research.ibm.com/articles/brain-chip,shtml, 2017.

[2]

Merolla, P.A., et al., A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014. 345(6197): p. 668--673.

[3]

Jaeger, H., The "echo state" approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report, 2001. 148(34): p. 13.

[4]

Maass, W., T. Natschläger, and H. Markram, Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural computation, 2002. 14(11): p. 2531--2560.

Digital Library

[5]

Schrauwen, B., D. Verstraeten, and J. Van Campenhout. An overview of reservoir computing: theory, applications and implementations. in Proceedings of the 15th European Symposium on Artificial Neural Networks. p. 471--482 2007. 2007.

[6]

Lukoševičius, M. and H. Jaeger, Reservoir computing approaches to recurrent neural network training. Computer Science Review, 2009. 3(3): p. 127--149.

Digital Library

[7]

Langton, C.G., Computation at the edge of chaos: phase transitions and emergent computation. Physica D: Nonlinear Phenomena, 1990. 42(1-3): p. 12--37.

Digital Library

[8]

Bertschinger, N. and T. Natschläger, Real-time computation at the edge of chaos in recurrent neural networks. Neural computation, 2004. 16(7): p. 1413--1436.

Digital Library

[9]

Li, J., et al. Analog hardware implementation of spike-based delayed feedback reservoir computing system. in Neural Networks (IJCNN), 2017 International Joint Conference on. 2017. IEEE.

[10]

Kitzbichler, M.G., et al., Broadband criticality of human brain network synchronization. PLoS computational biology, 2009. 5(3): p. e1000314.

[11]

Gourier, N., D. Hall, and J.L. Crowley. Estimating face orientation from robust detection of salient facial structures. in FG Net Workshop on Visual Observation of Deictic Gestures. 2004.

[12]

Kandel, E.R., et al., Principles of neural science. Vol. 4. 2000: McGraw-hill New York.

[13]

Boedecker, J., et al., Information processing in echo state networks at the edge of chaos. Theory in Biosciences, 2012. 131(3): p. 205--213.

[14]

Legenstein, R. and W. Maass, Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks, 2007. 20(3): p. 323--334.

Digital Library

[15]

Li, J., et al., Enabling Sustainable Cyber Physical Security Systems Through Neuromorphic Computing. IEEE Transactions on Sustainable Computing, 2017.

[16]

Zhao, C., et al. Novel spike based reservoir node design with high performance spike delay loop. in Proceedings of the 3rd ACM International Conference on Nanoscale Computing and Communication. 2016. ACM.

Digital Library

[17]

Ikeda, K. and K. Matsumoto, High-dimensional chaotic behavior in systems with time-delayed feedback. Physica D: Nonlinear Phenomena, 1987. 29(1-2): p. 223--235.

Digital Library

[18]

Hegger, R., et al., Identifying and modeling delay feedback systems. Physical review letters, 1998. 81(3): p. 558.

[19]

Legenstein, R. and W. Maass, What makes a dynamical system computationally powerful. New directions in statistical signal processing: From systems to brain, 2007: p. 127--154.

[20]

Mayor, J. and W. Gerstner, Signal buffering in random networks of spiking neurons: Microscopic versus macroscopic phenomena. Physical Review E, 2005. 72(5): p. 051906.

[21]

Junges, L. and J.A. Gallas, Intricate routes to chaos in the Mackey-Glass delayed feedback system. Physics letters A, 2012. 376(30): p. 2109--2116.

[22]

Li, J., et al. A Deep Learning Based Approach for Analog Hardware Implementation of Delayed Feedback Reservoir Computing System. in Proceedings of 19th International Symposium In Quality Electronic Design (ISQED). 2018. IEEE.

[23]

Bai, K. and Y. Yi. A Path to Energy-efficient Spiking Delayed Feedback Reservoir Computing System for Brain-inspired Neuromorphic Processors. in Proceedings of 19th International Symposium In Quality Electronic Design (ISQED). 2018. IEEE.

[24]

Zhao, C., et al., Spike-time-dependent encoding for neuromorphic processors. ACM Journal on Emerging Technologies in Computing Systems (JETC), 2015. 12(3): p. 23.

Digital Library

[25]

Zhao, C., J. Li, and Y. Yi. Making neural encoding robust and energy efficient: an advanced analog temporal encoder for brain-inspired computing systems. in Proceedings of the 35th International Conference on Computer-Aided Design. 2016. ACM.

Digital Library

Cited By

Bai KThiem CLombardi JLiang YYi Y(2023)Design Strategies and Applications of Reservoir Computing: Recent Trends and Prospects [Feature]IEEE Circuits and Systems Magazine10.1109/MCAS.2023.332549623:4(10-33)Online publication date: Dec-2024
https://doi.org/10.1109/MCAS.2023.3325496
Liu SYi Y(2023)Knowledge Distillation between DNN and SNN for Intelligent Sensing Systems on Loihi Chip2023 24th International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED57927.2023.10129306(1-8)Online publication date: 5-Apr-2023
https://doi.org/10.1109/ISQED57927.2023.10129306
Hamedani KLiu LHu SAshdown JWu JYi Y(2020)Detecting Dynamic Attacks in Smart Grids Using Reservoir Computing: A Spiking Delayed Feedback Reservoir Based ApproachIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2019.29028454:3(253-264)Online publication date: Jun-2020
https://doi.org/10.1109/TETCI.2019.2902845
Show More Cited By

Index Terms

Enabling a new era of brain-inspired computing: energy-efficient spiking neural network with ring topology
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Hardware
  1. Integrated circuits

Recommendations

Brain-inspired balanced tuning for spiking neural networks
IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence

Due to the nature of Spiking Neural Networks (SNNs), it is challenging to be trained by biologically plausible learning principles. The multi-layered SNNs are with non-differential neurons, temporary-centric synapses, which make them nearly impossible ...
Enabling An New Era of Brain-inspired Computing: Energy-efficient Spiking Neural Network with Ring Topology
2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC)
The reservoir computing, an emerging computing paradigm, has proven its benefit to multifarious applications. In this work, we successfully designed and fabricated an analog delayed feedback reservoir (DFR) chip. Measurement results demonstrate its rich ...
Brain-inspired hierarchical spiking neural network using unsupervised STDP rule for image classification
ICMLC '18: Proceedings of the 2018 10th International Conference on Machine Learning and Computing

The human brain has powerful unsupervised learning ability without needs of large amounts of labeled samples, so brain-inspired neural networks have gained great attention recently. In this paper, a hierarchical spiking neural network (HSNN) is proposed ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

DAC '18: Proceedings of the 55th Annual Design Automation Conference

June 2018

1089 pages

ISBN:9781450357005

DOI:10.1145/3195970

Copyright © 2018 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]

Sponsors

EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE Council on Electronic Design Automation (CEDA)

In-Cooperation

SIGBED: ACM Special Interest Group on Embedded Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Air Force Research Laboratory

Conference

DAC '18

Sponsor:

EDAC
SIGDA

DAC '18: The 55th Annual Design Automation Conference 2018

June 24 - 29, 2018

California, San Francisco

Acceptance Rates

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

Upcoming Conference

DAC '25

Sponsor:
sigda

62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

San Francisco , CA , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
556
Total Downloads

Downloads (Last 12 months)124
Downloads (Last 6 weeks)19

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Bai KThiem CLombardi JLiang YYi Y(2023)Design Strategies and Applications of Reservoir Computing: Recent Trends and Prospects [Feature]IEEE Circuits and Systems Magazine10.1109/MCAS.2023.332549623:4(10-33)Online publication date: Dec-2024
https://doi.org/10.1109/MCAS.2023.3325496
Liu SYi Y(2023)Knowledge Distillation between DNN and SNN for Intelligent Sensing Systems on Loihi Chip2023 24th International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED57927.2023.10129306(1-8)Online publication date: 5-Apr-2023
https://doi.org/10.1109/ISQED57927.2023.10129306
Hamedani KLiu LHu SAshdown JWu JYi Y(2020)Detecting Dynamic Attacks in Smart Grids Using Reservoir Computing: A Spiking Delayed Feedback Reservoir Based ApproachIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2019.29028454:3(253-264)Online publication date: Jun-2020
https://doi.org/10.1109/TETCI.2019.2902845
Bai KAn QYi Y(2019)Deep-DFRProceedings of the 56th Annual Design Automation Conference 201910.1145/3316781.3317796(1-6)Online publication date: 2-Jun-2019
https://dl.acm.org/doi/10.1145/3316781.3317796
Bai KYi Y(2018)DFRACM Journal on Emerging Technologies in Computing Systems10.1145/326465914:4(1-22)Online publication date: 6-Dec-2018
https://dl.acm.org/doi/10.1145/3264659

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten