skip to main content
10.1145/2463209.2488900acmconferencesArticle/Chapter ViewAbstractPublication PagesdacConference Proceedingsconference-collections
research-article

Accelerators for biologically-inspired attention and recognition

Published:29 May 2013Publication History

ABSTRACT

Video and image content has begun to play a growing role in many applications, ranging from video games to autonomous self-driving vehicles. In this paper, we present accelerators for gist-based scene recognition, saliency-based attention, and HMAX-based object recognition that have multiple uses and are based on the current understanding of the vision systems found in the visual cortex of the mammalian brain. By integrating them into a two-level hierarchical system, we improve recognition accuracy and reduce computational time. Results of our accelerator prototype on a multi-FPGA system show real-time performance and high recognition accuracy with large speedups over existing CPU, GPU and FPGA implementations.

References

  1. Caltech 101 Database for Object Classification. http://www.vision.caltech.edu/Image_Datasets/Caltech101/.Google ScholarGoogle Scholar
  2. Dinigroup DNSEAM-PCIE. http://www.dinigroup.com/new/DNSEAM_PCIE.php.Google ScholarGoogle Scholar
  3. Dinigroup DNV6F6--PCIE Documentation. http://www.dinigroup.com/product/data/DNV6F6PCIe/files/DNV6F6PCIe_v14_lo.pdf.Google ScholarGoogle Scholar
  4. Stanford Dataset for Scene Classification. http://vision.stanford.edu/fmriscenes/resources.html.Google ScholarGoogle Scholar
  5. USC iLab for GIST C++ Implementation. http://ilab.usc.edu/toolkit/documentation.shtml.Google ScholarGoogle Scholar
  6. C. Ackerman and L. Itti. Robot steering with spectral image information. Robotics, IEEE Transactions on, 21(2):247--251, april 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. P. Akselrod, F. Zhao, I. Derekli, C. Farabet, B. Martini, Y. LeCun, and E. Culurciello. Hardware accelerated visual attention algorithm. In Information Sciences and Systems (CISS), 2011 45th Annual Conference on, pages 1--6, march 2011.Google ScholarGoogle ScholarCross RefCross Ref
  8. S. Bae, Y. Cho, S. Park, K. M. Irick, Y. Jin, and V. Narayanan. An FPGA implementation of information theoretic visual-saliency system and its optimization. In Intl. Symp. on Field Programmable Custom Computing Machines, FCCM, pages 41--48, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. I. Biederman. Do background depth gradients facilitate object identification. Perception, 10:573--578, 1982.Google ScholarGoogle ScholarCross RefCross Ref
  10. M. DeBole, A. Maashri, M. Cotter, C.-L. Yu, C. Chakrabarti, and V. Narayanan. A Framework for Accelerating Neuromorphic-Vision Algorithms on FPGAs. In Computer-Aided Design (ICCAD), 2011 IEEE/ACM International Conference on, nov. 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. C. Farabet, B. Martini, P. Akselrod, S. Talay, Y. LeCun, and E. Culurciello. Hardware accelerated convolutional neural networks for synthetic vision systems. pages 257--260, may. 2010.Google ScholarGoogle Scholar
  12. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 20(11):1254--1259, nov. 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S. Kestur, M. Park, J. Sabarad, D. Dantara, V. Narayanan, Y. Chen, and D. Khosla. Emulating Mammalian Vision on Reconfigurable Hardware. In Intl. Symp. on Field Programmable Custom Computing Machines FCCM'12, May 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. Maashri, M. DeBole, M. Cotter, N. Chandramoorthy, Y. Xiao, V. Narayanan, and C. Chakrabarti. Accelerating neuromorphic vision algorithms for recognition. In Design Automation Conference (DAC), 2012 49th ACM/EDAC/IEEE, pages 579--584, june 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. J. Mutch, U. Knoblich, and T. Poggio. CNS: a GPU-based framework for simulating cortically-organized networks. Technical Report MIT-CSAIL-TR-2010-013/CBCL-286, Massachusetts Institute of Technology, Cambridge, MA, February 2010.Google ScholarGoogle Scholar
  16. J. Mutch and D. G. Lowe. Object class recognition and localization using sparse features with limited receptive fields. Intl. J. Comput. Vision, 80:45--57, October 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. A. Oliva and P. Schyns. Coarse blobs or fine edges? evidence that information diagnosticity changes the perception of complex visual stimuli. Cognit Psychol, 34(1):72--107, 1997.Google ScholarGoogle ScholarCross RefCross Ref
  18. M. Park, S. Kestur, J. Sabarad, V. Narayanan, and M. Irwin. An FPGA-based Accelerator for Cortical Object Classification. In Proc. of Design Automation and Test Conference and Exhibition DATE'12, Mar 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. J. Sabarad, S. Kestur, M. Park, D. Dantara, V. Narayanan, Y. Chen, and D. Khosla. A Reconfigurable Accelerator for Neuromorphic Object Recognition. In Proc. of Asia South Pacific Design Automation Conference ASPDAC'12, Jan 2012.Google ScholarGoogle ScholarCross RefCross Ref
  20. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio. Robust object recognition with cortex-like mechanisms. Pattern Analysis and Machine Intelligence, IEEE Tran on, 29(3):411--426, march 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. C. Siagian and L. Itti. Rapid biologically-inspired scene classification using features shared with visual attention. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 29(2):300--312, feb. 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. D. Thomas and W. Luk. Fpga accelerated simulation of biologically plausible spiking neural networks. In Field Programmable Custom Computing Machines, 2009. FCCM '09. 17th IEEE Symposium on, pages 45--52, april 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. A. Torralba. Modeling global scene factors in attention. JOSA - A, 20:1407--1418, 2003.Google ScholarGoogle Scholar
  24. T. Xu, T. Pototschnig, K. Kühnlenz, and M. Buss. A high-speed multi-gpu implementation of bottom-up attention using cuda. In ICRA'09: Proceedings of the 2009 IEEE international conference on Robotics and Automation, pages 1120--1126, Piscataway, NJ, USA, 2009. IEEE Press. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Accelerators for biologically-inspired attention and recognition

      Recommendations

      Comments

      Login options

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

      Sign in
      • Published in

        cover image ACM Conferences
        DAC '13: Proceedings of the 50th Annual Design Automation Conference
        May 2013
        1285 pages
        ISBN:9781450320719
        DOI:10.1145/2463209

        Copyright © 2013 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]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 29 May 2013

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate1,770of5,499submissions,32%

        Upcoming Conference

        DAC '24
        61st ACM/IEEE Design Automation Conference
        June 23 - 27, 2024
        San Francisco , CA , USA

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader