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Dynamically Reconfigurable Hardware for Evolving Bio-Inspired Architectures

Dynamically Reconfigurable Hardware for Evolving Bio-Inspired Architectures

Andres Upegui
Copyright: © 2010 |Pages: 22
ISBN13: 9781605667980|ISBN10: 1605667986|ISBN13 Softcover: 9781616924164|EISBN13: 9781605667997
DOI: 10.4018/978-1-60566-798-0.ch001
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MLA

Upegui, Andres. "Dynamically Reconfigurable Hardware for Evolving Bio-Inspired Architectures." Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, edited by Raymond Chiong, IGI Global, 2010, pp. 1-22. https://doi.org/10.4018/978-1-60566-798-0.ch001

APA

Upegui, A. (2010). Dynamically Reconfigurable Hardware for Evolving Bio-Inspired Architectures. In R. Chiong (Ed.), Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications (pp. 1-22). IGI Global. https://doi.org/10.4018/978-1-60566-798-0.ch001

Chicago

Upegui, Andres. "Dynamically Reconfigurable Hardware for Evolving Bio-Inspired Architectures." In Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, edited by Raymond Chiong, 1-22. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-798-0.ch001

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Abstract

During the last few years, reconfigurable computing devices have experienced an impressive development in their resource availability, speed, and configurability. Currently, commercial FPGAs offer the possibility of self-reconfiguring by partially modifying their configuration bit-string, providing high architectural flexibility, while guaranteeing high performance. On the other hand, we have bio-inspired hardware, a large research field taking inspiration from living beings in order to design hardware systems, which includes diverse approaches like evolvable hardware, neural hardware, and fuzzy hardware. Living beings are well known for their high adaptability to environmental changes, featuring very flexible adaptations at several levels. Bio-inspired hardware systems require such flexibility to be provided by the hardware platform on which the system is implemented. Even though some commercial FPGAs provide enhanced reconfigurability features such as partial and dynamic reconfiguration, their utilization is still in the early stages and they are not well supported by FPGA vendors, thus making their inclusion difficult in existing bio-inspired systems. This chapter presents a set of methodologies and architectures for exploiting the reconfigurability advantages of current commercial FPGAs in the design of bio-inspired hardware systems. Among the presented architectures are neural networks, spiking neuron models, fuzzy systems, cellular automata and Random Boolean Networks.

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