Accelerating neuromorphic vision algorithms for recognition

Authors:
Ahmed Al Maashri;Michael Debole;Matthew Cotter;Nandhini Chandramoorthy;Yang Xiao;Vijaykrishnan Narayanan;Chaitali Chakrabarti
Affiliations:
The Pennsylvania State University;IBM System and Technology Group;The Pennsylvania State University;The Pennsylvania State University;The Pennsylvania State University;The Pennsylvania State University;Arizona State University
Venue:
Proceedings of the 49th Annual Design Automation Conference
Year:
2012

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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.