Accelerating neuromorphic vision algorithms for recognition

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

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

39 Scopus citations


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 (US)
Title of host publicationProceedings of the 49th Annual Design Automation Conference, DAC '12
Number of pages6
StatePublished - Jul 11 2012
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
CountryUnited States
CitySan Francisco, CA

All Science Journal Classification (ASJC) codes

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

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