Genetic Algorithm based pipeline scheduling in high-level synthesis

Xiaohao Gao; Takeshi Yoshimura

doi:10.1109/ASICON.2013.6811982

Genetic Algorithm based pipeline scheduling in high-level synthesis

Xiaohao Gao, Takeshi Yoshimura

大学院情報生産システム研究科

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

In this work, we present a Genetic Algorithm (GA) based method for pipeline scheduling optimization. The objective is to minimize the circuit area under both data initiation interval and pipeline latency constraints. In the initialization, the scheduler generates a series of solutions between As Soon As Possible (ASAP) and As Late As Possible (ALAP) interval. Afterwards a Linear Programming (LP) algorithm is applied for transforming unfeasible solutions to feasible solutions, which are input to GA for searching the optimization result. In the experiments, our proposed algorithm achieves an average of 29.74% area improvement by comparing with ASAP and ALAP methods.

本文言語	English
ホスト出版物のタイトル	Proceedings of International Conference on ASIC
出版社	IEEE Computer Society
ISBN（印刷版）	9781467364157
DOI	https://doi.org/10.1109/ASICON.2013.6811982
出版ステータス	Published - 2013
イベント	2013 IEEE 10th International Conference on ASIC, ASICON 2013 - Shenzhen 継続期間: 2013 10月 28 → 2013 10月 31

Other

Other	2013 IEEE 10th International Conference on ASIC, ASICON 2013
City	Shenzhen
Period	13/10/28 → 13/10/31

ASJC Scopus subject areas

ハードウェアとアーキテクチャ
電子工学および電気工学

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10.1109/ASICON.2013.6811982

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引用スタイル

@inproceedings{30636ff273eb47788373aedd9207ad62,

title = "Genetic Algorithm based pipeline scheduling in high-level synthesis",

abstract = "In this work, we present a Genetic Algorithm (GA) based method for pipeline scheduling optimization. The objective is to minimize the circuit area under both data initiation interval and pipeline latency constraints. In the initialization, the scheduler generates a series of solutions between As Soon As Possible (ASAP) and As Late As Possible (ALAP) interval. Afterwards a Linear Programming (LP) algorithm is applied for transforming unfeasible solutions to feasible solutions, which are input to GA for searching the optimization result. In the experiments, our proposed algorithm achieves an average of 29.74% area improvement by comparing with ASAP and ALAP methods.",

author = "Xiaohao Gao and Takeshi Yoshimura",

year = "2013",

doi = "10.1109/ASICON.2013.6811982",

language = "English",

isbn = "9781467364157",

booktitle = "Proceedings of International Conference on ASIC",

publisher = "IEEE Computer Society",

note = "2013 IEEE 10th International Conference on ASIC, ASICON 2013 ; Conference date: 28-10-2013 Through 31-10-2013",

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AU - Gao, Xiaohao

AU - Yoshimura, Takeshi

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N2 - In this work, we present a Genetic Algorithm (GA) based method for pipeline scheduling optimization. The objective is to minimize the circuit area under both data initiation interval and pipeline latency constraints. In the initialization, the scheduler generates a series of solutions between As Soon As Possible (ASAP) and As Late As Possible (ALAP) interval. Afterwards a Linear Programming (LP) algorithm is applied for transforming unfeasible solutions to feasible solutions, which are input to GA for searching the optimization result. In the experiments, our proposed algorithm achieves an average of 29.74% area improvement by comparing with ASAP and ALAP methods.

AB - In this work, we present a Genetic Algorithm (GA) based method for pipeline scheduling optimization. The objective is to minimize the circuit area under both data initiation interval and pipeline latency constraints. In the initialization, the scheduler generates a series of solutions between As Soon As Possible (ASAP) and As Late As Possible (ALAP) interval. Afterwards a Linear Programming (LP) algorithm is applied for transforming unfeasible solutions to feasible solutions, which are input to GA for searching the optimization result. In the experiments, our proposed algorithm achieves an average of 29.74% area improvement by comparing with ASAP and ALAP methods.

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DO - 10.1109/ASICON.2013.6811982

M3 - Conference contribution

AN - SCOPUS:84901352270

SN - 9781467364157

BT - Proceedings of International Conference on ASIC

PB - IEEE Computer Society

T2 - 2013 IEEE 10th International Conference on ASIC, ASICON 2013

Y2 - 28 October 2013 through 31 October 2013

ER -

Genetic Algorithm based pipeline scheduling in high-level synthesis

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ASJC Scopus subject areas

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