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Integration of Optimization Approach Based on Multiple Wordlength Operation Grouping in the AAA Methodology for Real-Time Systems: LVQ Implementation

Integration of Optimization Approach Based on Multiple Wordlength Operation Grouping in the AAA Methodology for Real-Time Systems: LVQ Implementation

Ahmed Ghazi Blaiech, Khaled Ben Khalifa, Mohamed Boubaker, Mohamed Akil, Mohamed Hedi Bedoui
Copyright: © 2014 |Volume: 5 |Issue: 1 |Pages: 24
ISSN: 1947-3176|EISSN: 1947-3184|EISBN13: 9781466654198|DOI: 10.4018/ijertcs.2014010103
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MLA

Blaiech, Ahmed Ghazi, et al. "Integration of Optimization Approach Based on Multiple Wordlength Operation Grouping in the AAA Methodology for Real-Time Systems: LVQ Implementation." IJERTCS vol.5, no.1 2014: pp.37-60. http://doi.org/10.4018/ijertcs.2014010103

APA

Blaiech, A. G., Ben Khalifa, K., Boubaker, M., Akil, M., & Bedoui, M. H. (2014). Integration of Optimization Approach Based on Multiple Wordlength Operation Grouping in the AAA Methodology for Real-Time Systems: LVQ Implementation. International Journal of Embedded and Real-Time Communication Systems (IJERTCS), 5(1), 37-60. http://doi.org/10.4018/ijertcs.2014010103

Chicago

Blaiech, Ahmed Ghazi, et al. "Integration of Optimization Approach Based on Multiple Wordlength Operation Grouping in the AAA Methodology for Real-Time Systems: LVQ Implementation," International Journal of Embedded and Real-Time Communication Systems (IJERTCS) 5, no.1: 37-60. http://doi.org/10.4018/ijertcs.2014010103

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Abstract

The Multiple-Wordlength Operation Grouping (MWOG) is a recently used approach for an optimized implementation on a Field Programmable Gate Array (FPGA). By fixing the precision constraint, this approach allows minimizing the data wordlength. In this paper, the authors present the integration of the approach based on the MWOG in the Algorithm Architecture Adequation (AAA) methodology, designed to implement real-time applications onto reconfigurable circuits. This new AAA-MWOG methodology will improve the optimization phase of the AAA methodology by taking into account the data wordlength and creating approximative-wordlength operation groups, where the operations in the same group will be performed with the same operator. The AAA-MWOG methodology will allow a considerable gain of circuit resources. This contribution is demonstrated by implementing the Learning Vector Quantization (LVQ) neural-networks model on the FPGA. The LVQ optimization is used to quantify vigilance states starting from processing the electroencephalographic signal. The precision-gain relation has been studied and reported.

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