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

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Proceedings of International Conference on ASIC
Publisher	IEEE Computer Society
ISBN (Print)	9781467364157
DOIs	https://doi.org/10.1109/ASICON.2013.6811982
Publication status	Published - 2013
Event	2013 IEEE 10th International Conference on ASIC, ASICON 2013 - Shenzhen Duration: 2013 Oct 28 → 2013 Oct 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

Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/ASICON.2013.6811982

Cite this

@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",

}

TY - GEN

T1 - Genetic Algorithm based pipeline scheduling in high-level synthesis

AU - Gao, Xiaohao

AU - Yoshimura, Takeshi

PY - 2013

Y1 - 2013

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.

UR - http://www.scopus.com/inward/record.url?scp=84901352270&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901352270&partnerID=8YFLogxK

U2 - 10.1109/ASICON.2013.6811982

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

Abstract

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this