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A novel method for high-level synthesis of datapaths in digital filters using a moth-flame optimization algorithm

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Abstract

High-level synthesis (HLS) is one of the most important processes in digital VLSI circuit design. Owing to complexity and enormity of the design space in HLS problems, employing meta-heuristic methods and swarm intelligence has been considered as a highly favorable option when solving such problems. This research work proposes a moth-flame optimization (MFO) algorithm-based method for HLS of datapaths in digital filters, where scheduling, allocating, and binding steps were performed simultaneously. It was observed that the efficiency of the proposed method enjoyed an improved efficiency thanks to the mentioned simultaneous steps while being combined with the MFO algorithm. By comparing the performance of the proposed method with Genetic algorithm based method and particle swarm optimization based method for HLS of digital filters benchmarks, it can be inferred that the proposed method outperforms the other two methods in HLS of digital filters. This is evidently approved by a maximum improvement observed in the rates of the delay, the occupied area of the chip, and the power consumption for 2.99%, 6.58%, and 6.48%, respectively. In addition to the mentioned improvement, another striking characteristic of the proposed method is its fast runtime in reaching a response. This could significantly lower the costs while increasing the design speed of circuits having large dimensions. As well, an averagely 20% rise was also discerned in the algorithm runtime compared to the other two methods.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Birjand, Birjand, Iran

    Mohammad Reza Esmaeili, Seyed Hamid Zahiri & Seyed Mohammad Razavi

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  1. Mohammad Reza Esmaeili
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  2. Seyed Hamid Zahiri
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  3. Seyed Mohammad Razavi
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Correspondence to Seyed Hamid Zahiri.

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Esmaeili, M.R., Zahiri, S.H. & Razavi, S.M. A novel method for high-level synthesis of datapaths in digital filters using a moth-flame optimization algorithm. Evol. Intel. 13, 399–414 (2020). https://doi.org/10.1007/s12065-019-00302-w

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