Title :
Embedded SVM on TMS320C6713 for signal prediction in classification and regression applications
Author :
Zabalza, J. ; Jinchang Ren ; Clemente, C. ; Di Caterina, G. ; Soraghan, J.
Author_Institution :
Centre for Excellence in Signal & Image Process. (CeSIP), Univ. of Strathclyde, Glasgow, UK
Abstract :
Support Vector Machine (SVM) is a very powerful tool for signal prediction including classification and regression. With Texas Instruments TMS320C6713 DSK, an embedded SVM is implemented, where a user friendly interface is provided via peripherals like the DIPs and LEDs. The C6713 processor in combination with the SDRAM block memory can solve the complex computation that SVM requires. Also a Real-Time utilisation of the device from Matlab environment is demonstrated. An exciting application framework is finally obtained, from which some conclusions related to the implementation and final usage are derived.
Keywords :
DRAM chips; Texas Instruments computers; signal classification; support vector machines; C6713 processor; DIP; LED; Matlab environment; Real-Time utilisation; SDRAM block memory; TMS320C6713; Texas Instruments TMS320C6713 DSK; classification applications; complex computation; embedded SVM; regression applications; signal prediction; support vector machine; TMS320C6713; embedded system; signal prediction; support vector machine;
Conference_Titel :
Education and Research Conference (EDERC), 2012 5th European DSP
Conference_Location :
Amsterdam
Print_ISBN :
978-1-4673-4595-8
Electronic_ISBN :
978-1-4673-4595-8
DOI :
10.1109/EDERC.2012.6532232