research-article

Accelerating neuromorphic vision algorithms for recognition

Authors:

Ahmed Al Maashri,

Michael Debole,

Matthew Cotter,

Nandhini Chandramoorthy,

Vijaykrishnan Narayanan,

Chaitali ChakrabartiAuthors Info & Claims

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

Pages 579 - 584

https://doi.org/10.1145/2228360.2228465

Published: 03 June 2012 Publication History

Abstract

Video analytics introduce new levels of intelligence to automated scene understanding. Neuromorphic algorithms, such as HMAX, are proposed as robust and accurate algorithms that mimic the processing in the visual cortex of the brain. HMAX, for instance, is a versatile algorithm that can be repurposed to target several visual recognition applications. This paper presents the design and evaluation of hardware accelerators for extracting visual features for universal recognition. The recognition applications include object recognition, face identification, facial expression recognition, and action recognition. These accelerators were validated on a multi-FPGA platform and significant performance enhancement and power efficiencies were demonstrated when compared to CMP and GPU platforms. Results demonstrate as much as 7.6X speedup and 12.8X more power-efficient performance when compared to those platforms.

References

[1]

L. Itti, C. Koch, and E. Niebur, "A Model of Saliency-Based Visual Attention for Rapid Scene Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254--1259, Nov 1998.

Digital Library

[2]

M. Riesenhuber and T. Poggio, "Hierarchical Models of Object Recognition in Cortex," Nature Neuroscience, vol. 2, no. 11, pp. 1019--1025, November 1999.

[3]

T. Serre et al., "Robust Object Recognition With Cortex-Like Mechanisms," IEEE PAMI, vol. 29, no. 3, March 2007.

Digital Library

[4]

E. Meyers and L. Wolf, "Using Biologically Inspired Features for Face Processing," International Journal of Computer Vision, vol. 76, no. 1, pp. 93--104, January 2008.

Digital Library

[5]

J Mutch, U Knoblich, and T Poggio, "CNS: A GPU-Based Framework for Simulating Cortically-Organized Networks," Massachusetts Institute of Technology, Cambridge, MA, MIT-CSAIL-TR-2010-013/CBCL-286 2010.

[6]

H. Jhuang, T. Serre, L. Wolf, and T. Poggio, "A Biologically Inspired System for Action Recognition," in International Conference on Computer Vision (ICCV), 2007, pp. 1--8.

[7]

J. Mutch and D. G. Lowe, "Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields," International Journal of Computer Vision (IJCV), vol. 80, no. 1, October 2008.

Digital Library

[8]

S. Park et al., "System-On-Chip for Biologically Inspired Vision Applications," Information Processing Society of Japan, 2012, {In Press}.

[9]

L. Fei-Fei et al., "Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Tested on 101 Object Categories," in IEEE CVPR 2004, Workshop on Generative-Model Based Vision, 2004.

Digital Library

[10]

F. Samaria and A. Harter, "Parameterisation of a Stochastic Model for Human Face Identification," in 2nd IEEE Workshop on Applications of Computer Vision, 1994.

[11]

P. J. Phillips et al., "The FERET Evaluation Methodology for Face Recognition Algorithms," Trans. of Pattern Analysis and Machine Intelligence, vol. 22, no. 10, October 2000.

Digital Library

[12]

M. Lyons et al., "Coding Facial Expressions with Gabor Wavelets," in Third IEEE International Conference on Autmatomatic Face and Gesture Recognition, 1998.

Digital Library

[13]

M. Blank et al., "Actions as Space-Time Shapes," in International Conference on Computer Vision, 2005.

Digital Library

[14]

Xilinx, "Virtex-5 Family Overview," DS100(v5.0) 2009.

[15]

Nvidia. (2011) Tesla M2090 Board Specification. {Online}. http://www.nvidia.com/docs/IO/43395/Tesla-M2090-Board-Specification.pdf

[16]

I. Kuon and J. Rose, "Measuring the Gap Between FPGAs and ASICs," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 26, no. 2, pp. 203--215, February 2007.

Digital Library

[17]

J.-Y. Kim et al., "A 201.4 GOPS 496 mW Real-Time Multi-Object Recognition Processor With Bio-Inspired Neural Perception Engine," IEEE Journal of Solid-State Circuits, vol. 45, no. 1, pp. 32--45, Jan 2010.

[18]

C.-Y. Tsai et al., "A 1.0TOPS/W 36-Core Neocortical Computing Processor with 2.3Tb/s Kautz NoC for Universal Visual Recognition," in IEEE Int. Conference Digest of Technical Papers, San Francisco, 2012, pp. 480--482.

[19]

C. Farabet et al., "Hardware Accelerated Convolutional Neural Networks for Synthetic Vision Systems," in ISCAS, Paris, 2010, pp. 257--260.

[20]

R. Iyer et al., "CogniServe: Heterogeneous Server Architecture for Large-Scale Recognition," IEEE Micro, vol. 31, no. 3, pp. 20--31, May-June 2011.

Digital Library

[21]

J. Clemons et al., "EFFEX: An Embedded Processor for Computer Vision Based Feature Extraction," in The 48th ACM/EDAC/IEEE DAC, San Diego, 2011, pp. 1020--1024.

Digital Library

[22]

S. Chakradhar et al., "A Dynamically Configurable Coprocessor for Convolutional Neural Networks," in ISCA, 2010, pp. 247--257.

Digital Library

Cited By

Saleem MSenan NWahid FAamir MSamad AKhan M(2022)Comparative Analysis of Recent Architecture of Convolutional Neural NetworkMathematical Problems in Engineering10.1155/2022/73136122022(1-9)Online publication date: 31-Mar-2022
https://doi.org/10.1155/2022/7313612
Faez Abir MHassan Rokon KAsem SAbdur Rahman ABahadur NYousuf Ali M(2022)Paddy Disease Detection Using Deep Learning2022 25th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT57492.2022.10055701(61-66)Online publication date: 17-Dec-2022
https://doi.org/10.1109/ICCIT57492.2022.10055701
Rodriguez-Borbon JMa XRoy-Chowdhury ANajjar W(2020)Heterogeneous Acceleration of HAR ApplicationsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2019.289530430:3(888-902)Online publication date: Mar-2020
https://doi.org/10.1109/TCSVT.2019.2895304
Show More Cited By

Index Terms

Accelerating neuromorphic vision algorithms for recognition
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

Recommendations

Hardware Acceleration for Neuromorphic Vision Algorithms

Neuromorphic vision algorithms are biologically inspired models that follow the processing that takes place in the primate visual cortex. Despite their efficiency and robustness, the complexity of these algorithms results in reduced performance when ...
Face Recognition Based Person Specific Identification for Video Surveillance Applications
WCI '15: Proceedings of the Third International Symposium on Women in Computing and Informatics

Face detection is an important aspect for applications like biometrics, video surveillance and human computer interaction. Videos provide abundant information and also that can be leveraged by temporal variations in pose, expression changes and ...
Face recognition subject to variations in facial expression, illumination and pose using correlation filters

In this paper, we have selected some recent advanced correlation filters: minimum average correlation filter (MACE), unconstrained MACE filter (UMACE), phase-only unconstrained MACE filter (POUMACE), distance-classifier correlation filter (DCCF) [B.V.K. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

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]

Sponsors

EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CEDA

In-Cooperation

SIGBED: ACM Special Interest Group on Embedded Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 June 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

DAC '12

Sponsor:

EDAC
SIGDA

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

June 3 - 7, 2012

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

41
Total Citations
View Citations
556
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)1

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Saleem MSenan NWahid FAamir MSamad AKhan M(2022)Comparative Analysis of Recent Architecture of Convolutional Neural NetworkMathematical Problems in Engineering10.1155/2022/73136122022(1-9)Online publication date: 31-Mar-2022
https://doi.org/10.1155/2022/7313612
Faez Abir MHassan Rokon KAsem SAbdur Rahman ABahadur NYousuf Ali M(2022)Paddy Disease Detection Using Deep Learning2022 25th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT57492.2022.10055701(61-66)Online publication date: 17-Dec-2022
https://doi.org/10.1109/ICCIT57492.2022.10055701
Rodriguez-Borbon JMa XRoy-Chowdhury ANajjar W(2020)Heterogeneous Acceleration of HAR ApplicationsIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2019.289530430:3(888-902)Online publication date: Mar-2020
https://doi.org/10.1109/TCSVT.2019.2895304
Zhou SGuo QDu ZLiu DChen TLi LLiu SZhou JTemam OFeng XZhou XChen Y(2020)ParaML: A Polyvalent Multicore Accelerator for Machine LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.292752339:9(1764-1777)Online publication date: Sep-2020
https://doi.org/10.1109/TCAD.2019.2927523
Chen YLan HDu ZLiu STao JHan DLuo TGuo QLi LXie YChen T(2019)An Instruction Set Architecture for Machine LearningACM Transactions on Computer Systems10.1145/333146936:3(1-35)Online publication date: 13-Aug-2019
https://dl.acm.org/doi/10.1145/3331469
Li XZhang GWang ZZheng W(2019)HyConvIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.286429930:2(388-399)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1109/TPDS.2018.2864299
Billones RSybingco ELim LCulaba AVicerra RFillone ABandala ADadios E(2018)Vision-Based Passenger Activity Analysis System in Public Transport and Bus Stop Areas2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM)10.1109/HNICEM.2018.8666357(1-6)Online publication date: Nov-2018
https://doi.org/10.1109/HNICEM.2018.8666357
Li ZWang YZhi TChen T(2017)A survey of neural network acceleratorsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-6159-111:5(746-761)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s11704-016-6159-1
Liu SDu ZTao JHan DLuo TXie YChen YChen T(2016)CambriconACM SIGARCH Computer Architecture News10.1145/3007787.300117944:3(393-405)Online publication date: 18-Jun-2016
https://dl.acm.org/doi/10.1145/3007787.3001179
Chen YChen TXu ZSun NTemam O(2016)DianNao familyCommunications of the ACM10.1145/299686459:11(105-112)Online publication date: 28-Oct-2016
https://dl.acm.org/doi/10.1145/2996864
Show More Cited By

View Options

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents