Abstract:The design of an Evolvable Machine VHDL Core is presented, representing a discrete-time processing structure capable of supporting control system applications. This VHDL Core is implemented in an FPGA and is interfaced with an evolutionary algorithm implemented in firmware on a Digital Signal Processor (DSP) to create an evolvable system platform. The salient features of this architecture are presented. The capability to implement IIR filter structures is presented along with the results of the intrinsic evolu… Show more
“…In particular, structures of multiplierless filters were sought by many authors (Wade et al 1994;Hounsell et al 2004;Erba et al 2001;Gwaltney and Dutton 2005). Regardless of filter representation, the fitness function is usually constructed in the frequency domain.…”
“…In particular, structures of multiplierless filters were sought by many authors (Wade et al 1994;Hounsell et al 2004;Erba et al 2001;Gwaltney and Dutton 2005). Regardless of filter representation, the fitness function is usually constructed in the frequency domain.…”
“…The following list shows examples of evolvable systems which were implemented using the idea of the VRC on an FPGA: -evolvable image filter for the evolution of 3x3 image operators (EA is implemented either in PC [Zhang et al 2004] or on the same FPGA as a special circuit [Martinek and Sekanina 2005] or in an on-chip PowerPC processor [Vasicek and Sekanina 2007]); -evolvable sorting network for up to 28 inputs (EA is implemented on the same FPGA) [Korenek and Sekanina 2005]; -evolvable combinational circuits (EA is implemented on the same FPGA as a special circuit [Sekanina and Friedl 2004] or in the PowerPC processor on the same FPGA [Glette and Torresen 2005]); -intrinsic evolution of polymorphic combinational modules (EA is implemented on the same FPGA) ]; -evolvable IIR filter (EA is implemented on a DSP) [Gwaltney and Dutton 2005]; -packet classifiers (EA is implemented as a special circuit on the same FPGA) [Salomon et al 2006]. …”
“…Because a designer can construct the VRC so that it exactly fits the needs of a given evolvable hardware-based application, a perfect reconfigurable device can be obtained for a given problem. Examples include VRCs for the evolution of logic circuits [Sekanina and Friedl 2004], image filters [Martinek and Sekanina 2005], IIR filters [Gwaltney and Dutton 2005], sorting networks [Korenek and Sekanina 2005], or packet classifiers [Salomon et al 2006]. As the evolutionary algorithm can be implemented on the same FPGA (as an application-specific circuit or in a processor) in these applications, a fast configuration interface can be established connecting the configuration memory of VRC with chromosomes of EA.…”
A virtual reconfigurable circuit (VRC) is a domain-specific reconfigurable device developed using an ordinary FPGA in order to easily implement evolvable hardware applications. While a fast partial runtime reconfiguration and application-specific programmable elements represent the main advantages of VRC, the main disadvantage of the VRC is the area consumed. This study describes experiments conducted to estimate how the use of VRC influences the dependability of FPGAbased evolvable systems. It is shown that these systems are not as sensitive to faults as their area-demanding implementations might suggest. An evolutionary algorithm is utilized to design fault tolerant circuits as well as to perform an automatic functional recovery when faults are detected in the configuration memory of the FPGA. All the experiments are performed on models of reconfigurable devices.
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.
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