NDLI: Saliency-driven dynamic configuration of HMAX for energy-efficient multi-object recognition

Content Provider	IEEE Xplore Digital Library
Author	Sungho Park Al Maashri, A. Yang Xiao Irick, K.M. Narayanan, V.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA, USA (Sungho Park; Al Maashri, A.; Yang Xiao; Irick, K.M.; Narayanan, V.)
Abstract	Object recognition is one of the most important tasks in computer vision due to its wide variety of applications from small hand-held devices to surveillance systems in large public facilities. Even though biologically inspired approaches have been recently revealed to take another significant step forward to reduce its large power consumption, it still consumes relatively large amounts of energy because of the immense amount of data and computations. Typically in such biologically inspired - often called neuromorphic - object recognition implementations, visual saliency feeds feature extraction to limit the amount of computations effectively by picking a pre-determined size of patches around salient locations of an image. In this work, we explore the design space of HMAX for neuromorphic feature-extraction and classification along with the trade-off between energy consumption and classification accuracy. In addition, a novel method to further reduce energy consumption is proposed by leveraging effort-level of HMAX according to the findings of visual saliency in an efficient manner. Experiments revealed that our dynamic configuration achieved 70.57% of energy reduction with only 1.05% of accuracy loss for accuracy-critical applications. For energy-critical applications, a proposed configurations trades off 5.07% accuracy to gain 91.72% reduction in energy consumption.
Starting Page	139
Ending Page	144
File Size	2047436
Page Count	6
File Format	PDF
ISBN	9781479913312
ISSN	21593469
DOI	10.1109/ISVLSI.2013.6654636
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-05
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Prototypes Energy consumption Feature extraction Visualization Space exploration Kernel energy efficiency visual saliency HMAX object recognition FPGA dynamic configuration
Content Type	Text
Resource Type	Article

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