Hardware acceleration for neuromorphic vision algorithms

Ahmed Al Maashri, Matthew Cotter, Nandhini Chandramoorthy, Michael DeBole, Chi Li Yu, Vijaykrishnan Narayanan, Chaitali Chakrabarti

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)163-175
Number of pages13
JournalJournal of Signal Processing Systems
Volume70
Issue number2
DOIs
Publication statusPublished - Feb 2013

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Keywords

  • Domain-specific acceleration Power efficiency Neuromorphic systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modelling and Simulation
  • Hardware and Architecture

Cite this

Al Maashri, A., Cotter, M., Chandramoorthy, N., DeBole, M., Yu, C. L., Narayanan, V., & Chakrabarti, C. (2013). Hardware acceleration for neuromorphic vision algorithms. Journal of Signal Processing Systems, 70(2), 163-175. https://doi.org/10.1007/s11265-012-0699-x