Improving energy efficiency in handheld biometric applications

David C. Hoyle; John W. Gale; Robert C. Schultz; Ryan N. Rakvic; Robert W. Ives

doi:10.1117/12.918929

8 May 2012 Improving energy efficiency in handheld biometric applications

David C. Hoyle, John W. Gale, Robert C. Schultz, Ryan N. Rakvic, Robert W. Ives

Proceedings Volume 8406, Mobile Multimedia/Image Processing, Security, and Applications 2012; 84060N (2012) https://doi.org/10.1117/12.918929
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

With improved smartphone and tablet technology, it is becoming increasingly feasible to implement powerful biometric recognition algorithms on portable devices. Typical iris recognition algorithms, such as Ridge Energy Direction (RED), utilize two-dimensional convolution in their implementation. This paper explores the energy consumption implications of 12 different methods of implementing two-dimensional convolution on a portable device. Typically, convolution is implemented using floating point operations. If a given algorithm implemented integer convolution vice floating point convolution, it could drastically reduce the energy consumed by the processor. The 12 methods compared include 4 major categories: Integer C, Integer Java, Floating Point C, and Floating Point Java. Each major category is further divided into 3 implementations: variable size looped convolution, static size looped convolution, and unrolled looped convolution. All testing was performed using the HTC Thunderbolt with energy measured directly using a Tektronix TDS5104B Digital Phosphor oscilloscope. Results indicate that energy savings as high as 75% are possible by using Integer C versus Floating Point C. Considering the relative proportion of processing time that convolution is responsible for in a typical algorithm, the savings in energy would likely result in significantly greater time between battery charges.

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David C. Hoyle, John W. Gale, Robert C. Schultz, Ryan N. Rakvic, and Robert W. Ives "Improving energy efficiency in handheld biometric applications", Proc. SPIE 8406, Mobile Multimedia/Image Processing, Security, and Applications 2012, 84060N (8 May 2012); https://doi.org/10.1117/12.918929

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KEYWORDS

Convolution

Lab on a chip

Chemical mechanical planarization

Java

Biometrics

Detection and tracking algorithms

Oscilloscopes

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