research-article

An extrinsic EHW system for the evolutionary optimization and design of sequential circuit

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Yuzhen ZhangAuthors Info & Claims

AICCC '18: Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference

Pages 174 - 180

https://doi.org/10.1145/3299819.3299832

Published: 21 December 2018 Publication History

Abstract

The main obstacles in the evolutionary design of sequential circuits are the state assignment and the large evolution time for a complete circuit. In this paper, in order to minimize evolution time, a genetic algorithm (GA) based on a cost evolution of the circuit evolution is proposed to evolve a state assignment, which can lead to complexity reduction. A cost evaluation of the circuit evolution is uniquely defined as the fitness function of state assignment candidates. Under the GA-evolved state assignment, a novel LUT-based circuit evolution (LCE) is proposed to improve the search for a complete circuit. An extrinsic EHW system namely GALCE, which combines GA and LCE, aims to the evolutionary optimization and design of sequential circuit. This system is tested extensively on eight sequential circuits. The simulation results demonstrate the proposed approach can perform better in terms of average evolution time reduction and success rate.

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Cited By

Shang QChen LCui JLu Y(2020)Hardware Evolution Based on Improved Simulated Annealing Algorithm in Cyclone V FPSoCsIEEE Access10.1109/ACCESS.2020.29849508(64770-64782)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2984950
Kemcha RNedjah NMaouche ABougherara M(2019)Evolutionary Design of Approximate Sequential Circuits at RTL Using Particle Swarm OptimizationComputational Science and Its Applications – ICCSA 201910.1007/978-3-030-24296-1_54(671-684)Online publication date: 29-Jun-2019
https://doi.org/10.1007/978-3-030-24296-1_54

Index Terms

An extrinsic EHW system for the evolutionary optimization and design of sequential circuit
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Evolvable hardware

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cover image ACM Other conferences

AICCC '18: Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference

December 2018

206 pages

ISBN:9781450366236

DOI:10.1145/3299819

Copyright © 2018 ACM.

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Published: 21 December 2018

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AICCC '18

AICCC '18: 2018 Artificial Intelligence and Cloud Computing Conference

December 21 - 23, 2018

Tokyo, Japan

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Cited By

Shang QChen LCui JLu Y(2020)Hardware Evolution Based on Improved Simulated Annealing Algorithm in Cyclone V FPSoCsIEEE Access10.1109/ACCESS.2020.29849508(64770-64782)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2984950
Kemcha RNedjah NMaouche ABougherara M(2019)Evolutionary Design of Approximate Sequential Circuits at RTL Using Particle Swarm OptimizationComputational Science and Its Applications – ICCSA 201910.1007/978-3-030-24296-1_54(671-684)Online publication date: 29-Jun-2019
https://doi.org/10.1007/978-3-030-24296-1_54

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