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Synchronous Finite State Machines Design with Quantum-Inspired Evolutionary Computation

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Hardware for Soft Computing and Soft Computing for Hardware

Part of the book series: Studies in Computational Intelligence ((SCI,volume 529))

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Abstract

Synchronous finite state machines are very important for digital sequential designs. Among other important aspects, they represent a powerful way for synchronizing hardware components so that these components may cooperate adequately in the fulfillment of the main objective of the hardware design. In this chapter, we propose an evolutionary methodology based on the principles of quantum computing to synthesize finite state machines. First, we optimally solve the state assignment NP-complete problem, which is inherent to designing any synchronous finite state machines. This is motivated by the fact that with an optimal state assignment, one can physically implement the state machine in question using a minimal hardware area and response time. Second, with the optimal state assignment provided, we propose to use the same evolutionary methodology to yield an optimal evolutionary hardware that implements the state machine control component. The evolved hardware requires a minimal hardware area and imposes a minimal propagation delay on the machine output signals.

This chapter was developed together with Marcos Paulo Mello Araujo.

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Authors and Affiliations

  1. Department of Electronics Engineering and Telecommunications Faculty of Engineering , State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Nadia Nedjah

  2. Department of Systems Engineering and Computation Faculty of Engineering , State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Luiza de Macedo Mourelle

Authors
  1. Nadia Nedjah
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  2. Luiza de Macedo Mourelle
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Correspondence to Nadia Nedjah .

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Nedjah, N., de Macedo Mourelle, L. (2014). Synchronous Finite State Machines Design with Quantum-Inspired Evolutionary Computation. In: Hardware for Soft Computing and Soft Computing for Hardware. Studies in Computational Intelligence, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-319-03110-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-03110-1_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03109-5

  • Online ISBN: 978-3-319-03110-1

  • eBook Packages: EngineeringEngineering (R0)

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