Fine-grain instruction scheduling for low energy

W. Xu; A. Parikh; Mahmut Kandemir; Mary Jane Irwin

doi:10.1109/SIPS.2002.1049719

Fine-grain instruction scheduling for low energy

W. Xu, A. Parikh, Mahmut Kandemir, Mary Jane Irwin

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

Abstract

Energy consumption is increasingly becoming an important metric in designing computing systems. This paper presents an instruction scheduling algorithm to reduce energy consumption in processor data-paths. The unique aspect of our algorithm is that it is fine-granular; i.e., it works on a pipeline stage granularity. This is in contrast to current energy-aware instruction scheduling techniques that work on an instruction granularity. Our preliminary experimental results indicate that our fine-granular approach both leads to schedules with lower energy consumption (as compared to coarse-grain techniques) and helps us estimate the absolute data-path energy consumed by the code better.

Original language	English (US)
Title of host publication	IEEE Workshop on Signal Processing Systems, SIPS 2002
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	258-263
Number of pages	6
ISBN (Electronic)	0780375874
DOIs	https://doi.org/10.1109/SIPS.2002.1049719
State	Published - Jan 1 2002
Event	16th IEEE Workshop on Signal Processing Systems, SIPS 2002 - San Diego, United States Duration: Oct 16 2002 → Oct 18 2002

Publication series

Name	IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
Volume	2002-January
ISSN (Print)	1520-6130

Other

Other	16th IEEE Workshop on Signal Processing Systems, SIPS 2002
Country/Territory	United States
City	San Diego
Period	10/16/02 → 10/18/02

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Signal Processing
Applied Mathematics
Hardware and Architecture

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/SIPS.2002.1049719

Cite this

Xu, W., Parikh, A., Kandemir, M., & Irwin, M. J. (2002). Fine-grain instruction scheduling for low energy. In IEEE Workshop on Signal Processing Systems, SIPS 2002 (pp. 258-263). Article 1049719 (IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation; Vol. 2002-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIPS.2002.1049719

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title = "Fine-grain instruction scheduling for low energy",

abstract = "Energy consumption is increasingly becoming an important metric in designing computing systems. This paper presents an instruction scheduling algorithm to reduce energy consumption in processor data-paths. The unique aspect of our algorithm is that it is fine-granular; i.e., it works on a pipeline stage granularity. This is in contrast to current energy-aware instruction scheduling techniques that work on an instruction granularity. Our preliminary experimental results indicate that our fine-granular approach both leads to schedules with lower energy consumption (as compared to coarse-grain techniques) and helps us estimate the absolute data-path energy consumed by the code better.",

author = "W. Xu and A. Parikh and Mahmut Kandemir and Irwin, {Mary Jane}",

year = "2002",

month = jan,

day = "1",

doi = "10.1109/SIPS.2002.1049719",

language = "English (US)",

series = "IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "258--263",

booktitle = "IEEE Workshop on Signal Processing Systems, SIPS 2002",

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note = "16th IEEE Workshop on Signal Processing Systems, SIPS 2002 ; Conference date: 16-10-2002 Through 18-10-2002",

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Xu, W, Parikh, A, Kandemir, M & Irwin, MJ 2002, Fine-grain instruction scheduling for low energy. in IEEE Workshop on Signal Processing Systems, SIPS 2002., 1049719, IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation, vol. 2002-January, Institute of Electrical and Electronics Engineers Inc., pp. 258-263, 16th IEEE Workshop on Signal Processing Systems, SIPS 2002, San Diego, United States, 10/16/02. https://doi.org/10.1109/SIPS.2002.1049719

Fine-grain instruction scheduling for low energy. / Xu, W.; Parikh, A.; Kandemir, Mahmut et al.
IEEE Workshop on Signal Processing Systems, SIPS 2002. Institute of Electrical and Electronics Engineers Inc., 2002. p. 258-263 1049719 (IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation; Vol. 2002-January).

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

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N2 - Energy consumption is increasingly becoming an important metric in designing computing systems. This paper presents an instruction scheduling algorithm to reduce energy consumption in processor data-paths. The unique aspect of our algorithm is that it is fine-granular; i.e., it works on a pipeline stage granularity. This is in contrast to current energy-aware instruction scheduling techniques that work on an instruction granularity. Our preliminary experimental results indicate that our fine-granular approach both leads to schedules with lower energy consumption (as compared to coarse-grain techniques) and helps us estimate the absolute data-path energy consumed by the code better.

AB - Energy consumption is increasingly becoming an important metric in designing computing systems. This paper presents an instruction scheduling algorithm to reduce energy consumption in processor data-paths. The unique aspect of our algorithm is that it is fine-granular; i.e., it works on a pipeline stage granularity. This is in contrast to current energy-aware instruction scheduling techniques that work on an instruction granularity. Our preliminary experimental results indicate that our fine-granular approach both leads to schedules with lower energy consumption (as compared to coarse-grain techniques) and helps us estimate the absolute data-path energy consumed by the code better.

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BT - IEEE Workshop on Signal Processing Systems, SIPS 2002

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Fine-grain instruction scheduling for low energy

Abstract

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Other

All Science Journal Classification (ASJC) codes

UN SDGs

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