Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling

Salvador Perea, Rubén and Moreno González, Félix Antonio and Riesgo Alcaide, Teresa and Vidal, Alberto and Sekanina, Lukás (2012). Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling. "Microprocessors and Microsystems", v. 36 (n. 5); pp. 427-438. ISSN 0141-9331. https://doi.org/10.1016/j.micpro.2012.02.002.

Description

Title: Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling
Author/s:
  • Salvador Perea, Rubén
  • Moreno González, Félix Antonio
  • Riesgo Alcaide, Teresa
  • Vidal, Alberto
  • Sekanina, Lukás
Item Type: Article
Título de Revista/Publicación: Microprocessors and Microsystems
Date: July 2012
ISSN: 0141-9331
Volume: 36
Subjects:
Freetext Keywords: Evolvable hardware, FPGA, Bio-inspired architectures, Adaptive embedded systems, Adaptive image compression, Evolutionary Computation, Evolved wavelet transforms, Filter optimization
Faculty: E.U.I.T. Telecomunicación (UPM)
Department: Sistemas Electrónicos y de Control [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Adaptive embedded systems are required in various applications. This work addresses these needs in the area of adaptive image compression in FPGA devices. A simplified version of an evolution strategy is utilized to optimize wavelet filters of a Discrete Wavelet Transform algorithm. We propose an adaptive image compression system in FPGA where optimized memory architecture, parallel processing and optimized task scheduling allow reducing the time of evolution. The proposed solution has been extensively evaluated in terms of the quality of compression as well as the processing time. The proposed architecture reduces the time of evolution by 44% compared to our previous reports while maintaining the quality of compression unchanged with respect to existing implementations. The system is able to find an optimized set of wavelet filters in less than 2 min whenever the input type of data changes.

More information

Item ID: 15939
DC Identifier: http://oa.upm.es/15939/
OAI Identifier: oai:oa.upm.es:15939
DOI: 10.1016/j.micpro.2012.02.002
Official URL: http://www.sciencedirect.com/science/article/pii/S0141933112000191
Deposited by: Memoria Investigacion
Deposited on: 18 Nov 2013 18:54
Last Modified: 23 Feb 2017 17:31
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