Publication Details
Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling
Vidal Alberto
Moreno Felix
Riesgo Teresa
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Evolvable hardware, FPGA, Bio-inspired architectures, Adaptive embedded systems, Adaptive image compression, Evolutionary Computation, Evolved wavelet transforms, Filter optimization
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.
@article{BUT96948,
author="Ruben {Salvador} and Alberto {Vidal} and Felix {Moreno} and Teresa {Riesgo} and Lukáš {Sekanina}",
title="Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling",
journal="Microprocessors and Microsystems",
year="2012",
volume="36",
number="5",
pages="427--438",
doi="10.1016/j.micpro.2012.02.002",
issn="0141-9331",
url="http://www.sciencedirect.com/science/article/pii/S0141933112000191"
}