Accelerating neuromorphic vision algorithms for recognition

Ahmed Al Maashri*, Michael DeBole, Matthew Cotter, Nandhini Chandramoorthy, Yang Xiao, Vijaykrishnan Narayanan, Chaitali Chakrabarti

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the 49th Annual Design Automation Conference, DAC '12
Number of pages6
Publication statusPublished - 2012
Externally publishedYes
Event49th Annual Design Automation Conference, DAC '12 - San Francisco, CA, United States
Duration: Jun 3 2012Jun 7 2012

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X


Other49th Annual Design Automation Conference, DAC '12
Country/TerritoryUnited States
CitySan Francisco, CA


  • domain-specific acceleration
  • heterogeneous system
  • power efficiency
  • recognition

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modelling and Simulation


Dive into the research topics of 'Accelerating neuromorphic vision algorithms for recognition'. Together they form a unique fingerprint.

Cite this