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Hardware Acceleration for Neuromorphic Vision Algorithms

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Abstract

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 executed on general purpose processors. This paper proposes an application-specific system for accelerating a neuromorphic vision system for object recognition. The system is based on HMAX, a biologically-inspired model of the visual cortex. The neuromorphic accelerators are validated on a multi-FPGA system. Results show that the neuromorphic accelerators are 13.8× (2.6×) more power efficient when compared to CPU (GPU) implementation.

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Notes

  1. Personal communication with Jim Mutch, CBCL, MIT

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Acknowledgments

The authors would like to thank the reviewers for their valuable comments and suggestions. The authors would like to thank Yang Xiao, Penn State, for his role in developing the inter-FPGA communication for the prototyping platform. Also, the authors would like to thank Jim Mutch, MIT for his help in providing the most up to date implementation of the HMAX model. This work was funded in part by DARPA’s NeoVision 2 program, and NSF Awards 1147388, 0916887, 0903432. Ahmed Al Maashri is sponsored by a scholarship from the Government of Oman.

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Authors and Affiliations

  1. Microsystems Design Laboratory, The Pennsylvania State University, University Park, PA, USA

    Ahmed Al Maashri, Matthew Cotter, Nandhini Chandramoorthy & Vijaykrishnan Narayanan

  2. IBM Systems and Technology Group, Poughkeepsie, NY, USA

    Michael DeBole

  3. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

    Chi-Li Yu & Chaitali Chakrabarti

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  1. Ahmed Al Maashri
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  2. Matthew Cotter
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  3. Nandhini Chandramoorthy
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  4. Michael DeBole
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  5. Chi-Li Yu
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  6. Vijaykrishnan Narayanan
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  7. Chaitali Chakrabarti
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Corresponding author

Correspondence to Ahmed Al Maashri.

Additional information

This work was completed while Michael DeBole was at The Pennsylvania State University.

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Al Maashri, A., Cotter, M., Chandramoorthy, N. et al. Hardware Acceleration for Neuromorphic Vision Algorithms. J Sign Process Syst 70, 163–175 (2013). https://doi.org/10.1007/s11265-012-0699-x

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  • DOI: https://doi.org/10.1007/s11265-012-0699-x

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