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 journalArticlepeer-review

5 Scopus citations


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 (US)
Pages (from-to)163-175
Number of pages13
JournalJournal of Signal Processing Systems
Issue number2
StatePublished - Feb 2013

All Science Journal Classification (ASJC) codes

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


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