Accelerating neuromorphic vision algorithms for recognition
- 3 June 2012
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 579-584
- https://doi.org/10.1145/2228360.2228465
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.Keywords
Funding Information
- National Science Foundation (1.15E+27)
This publication has 12 references indexed in Scilit:
- EFFEXPublished by Association for Computing Machinery (ACM) ,2011
- A dynamically configurable coprocessor for convolutional neural networksPublished by Association for Computing Machinery (ACM) ,2010
- Object Class Recognition and Localization Using Sparse Features with Limited Receptive FieldsInternational Journal of Computer Vision, 2008
- Using Biologically Inspired Features for Face ProcessingInternational Journal of Computer Vision, 2007
- Measuring the Gap Between FPGAs and ASICsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2007
- Robust Object Recognition with Cortex-Like MechanismsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
- Actions as space-time shapesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- The FERET evaluation methodology for face-recognition algorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2000
- Hierarchical models of object recognition in cortexNature Neuroscience, 1999
- A model of saliency-based visual attention for rapid scene analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1998