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시간적 근접성 향상을 통한 효율적인 SVM 기반 음성/음악 분류기의 구현 방법

Title
시간적 근접성 향상을 통한 효율적인 SVM 기반 음성/음악 분류기의 구현 방법
Other Titles
Efficient Implementation of SVM-Based Speech/Music Classifier by Utilizing Temporal Locality
Author
장준혁
Keywords
Support Vector Machine (SVM); Selectable Mode Vocoder (SMV); Speech/Muisc Classification Algorithm; Embedded System; Memory; Cache
Issue Date
2012-03
Publisher
大韓電子工學會
Citation
전자공학회논문지 - SP, Vol.49, No.2 [2012], p149-156
Abstract
Support vector machines (SVMs) are well known for their pattern recognition capability, but proper care should be taken to alleviate their inherent implementation cost resulting from high computational intensity and memory requirement, especially in embedded systems where only limited resources are available. Since the memory requirement determined by the dimensionality and the number of support vectors is generally too high for a cache in embedded systems to accomodate, frequent accesses to the main memory occur inevitably whenever the cache is not able to provide requested data to the processor. These frequent accesses to the main memory result in overall performance degradation and increased energy consumption because a memory access typically takes longer and consumes more energy than a cache access or a register access. In this paper, we propose a technique that reduces the number of main memory accesses by optimizing the data access pattern of the SVM-based classifier in such a way that the temporal locality of the accesses increases, fully utilizing data loaded into the processor chip. With experiments, we confirm the enhancement made by the proposed technique in terms of the number of memory accesses, overall execution time, and energy consumption.
URI
https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART001643684http://hdl.handle.net/20.500.11754/67804
ISSN
1229-6384
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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