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

34 Citations (Scopus)

Abstract

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
Pages579-584
Number of pages6
DOIs
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

Other

Other49th Annual Design Automation Conference, DAC '12
CountryUnited States
CitySan Francisco, CA
Period6/3/126/7/12

Fingerprint

Particle accelerators
Hardware Accelerator
Facial Expression Recognition
Action Recognition
Visual Cortex
Object recognition
Object Recognition
Accelerator
Field Programmable Gate Array
Field programmable gate arrays (FPGA)
Brain
Speedup
Enhancement
Face
Hardware
Target
Evaluation
Processing
Demonstrate
Vision

All Science Journal Classification (ASJC) codes

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

Cite this

Al Maashri, A., DeBole, M., Cotter, M., Chandramoorthy, N., Xiao, Y., Narayanan, V., & Chakrabarti, C. (2012). Accelerating neuromorphic vision algorithms for recognition. In Proceedings of the 49th Annual Design Automation Conference, DAC '12 (pp. 579-584). (Proceedings - Design Automation Conference). https://doi.org/10.1145/2228360.2228465
Al Maashri, Ahmed ; DeBole, Michael ; Cotter, Matthew ; Chandramoorthy, Nandhini ; Xiao, Yang ; Narayanan, Vijaykrishnan ; Chakrabarti, Chaitali. / Accelerating neuromorphic vision algorithms for recognition. Proceedings of the 49th Annual Design Automation Conference, DAC '12. 2012. pp. 579-584 (Proceedings - Design Automation Conference).
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Al Maashri, A, DeBole, M, Cotter, M, Chandramoorthy, N, Xiao, Y, Narayanan, V & Chakrabarti, C 2012, Accelerating neuromorphic vision algorithms for recognition. in Proceedings of the 49th Annual Design Automation Conference, DAC '12. Proceedings - Design Automation Conference, pp. 579-584, 49th Annual Design Automation Conference, DAC '12, San Francisco, CA, United States, 6/3/12. https://doi.org/10.1145/2228360.2228465

Accelerating neuromorphic vision algorithms for recognition. / Al Maashri, Ahmed; DeBole, Michael; Cotter, Matthew; Chandramoorthy, Nandhini; Xiao, Yang; Narayanan, Vijaykrishnan; Chakrabarti, Chaitali.

Proceedings of the 49th Annual Design Automation Conference, DAC '12. 2012. p. 579-584 (Proceedings - Design Automation Conference).

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

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Al Maashri A, DeBole M, Cotter M, Chandramoorthy N, Xiao Y, Narayanan V et al. Accelerating neuromorphic vision algorithms for recognition. In Proceedings of the 49th Annual Design Automation Conference, DAC '12. 2012. p. 579-584. (Proceedings - Design Automation Conference). https://doi.org/10.1145/2228360.2228465