Go Green Radio Astronomy: Approximate Computing Perspective: Opportunities and Challenges

Ghayoor Gillani; Andre B.J. Kokkeler

doi:10.1145/3310273.3323427

Go Green Radio Astronomy: Approximate Computing Perspective: Opportunities and Challenges

Ghayoor Gillani, Andre B.J. Kokkeler

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

52 Downloads (Pure)

Abstract

Modern radio telescopes require highly energy/power-efficient computing systems. Signal processing pipelines of such radio telescopes are dominated by accumulation based iterative processes. As the input signal received at a radio telescope is regarded as Gaussian noise, employing approximate computing looks promising. Therefore, we present opportunities and challenges offered by the approximate computing paradigm to achieve the required efficiency targets.

Original language	English
Title of host publication	ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings
Place of Publication	New York, NY
Publisher	Association for Computing Machinery
Pages	300-301
Number of pages	2
ISBN (Electronic)	978-1-4503-6685-4
DOIs	https://doi.org/10.1145/3310273.3323427
Publication status	Published - 30 Apr 2019
Event	16th ACM International Conference on Computing Frontiers 2019 - Alghero, Italy Duration: 30 Apr 2019 → 2 May 2019 Conference number: 16 http://www.computingfrontiers.org/2019/

Conference

Conference	16th ACM International Conference on Computing Frontiers 2019
Abbreviated title	CF 2019
Country/Territory	Italy
City	Alghero
Period	30/04/19 → 2/05/19
Internet address	http://www.computingfrontiers.org/2019/

Keywords

Approximate computing
Energy efficiency
Iterative workloads
Power efficiency
Radio astronomy
2024 OA procedure

Access to Document

10.1145/3310273.3323427

ArticleFinal published version, 428 KBLicence: Taverne

Cite this

@inproceedings{b0c630951ce54fc0b573c0179b7eb604,

title = "Go Green Radio Astronomy: Approximate Computing Perspective: Opportunities and Challenges",

abstract = "Modern radio telescopes require highly energy/power-efficient computing systems. Signal processing pipelines of such radio telescopes are dominated by accumulation based iterative processes. As the input signal received at a radio telescope is regarded as Gaussian noise, employing approximate computing looks promising. Therefore, we present opportunities and challenges offered by the approximate computing paradigm to achieve the required efficiency targets.",

keywords = "Approximate computing, Energy efficiency, Iterative workloads, Power efficiency, Radio astronomy, 2024 OA procedure",

author = "Ghayoor Gillani and Kokkeler, {Andre B.J.}",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 16th ACM International Conference on Computing Frontiers 2019, CF 2019 ; Conference date: 30-04-2019 Through 02-05-2019",

year = "2019",

month = apr,

day = "30",

doi = "10.1145/3310273.3323427",

language = "English",

pages = "300--301",

booktitle = "ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings",

publisher = "Association for Computing Machinery",

address = "United States",

url = "http://www.computingfrontiers.org/2019/",

}

Gillani, G & Kokkeler, ABJ 2019, Go Green Radio Astronomy: Approximate Computing Perspective: Opportunities and Challenges. in ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings. Association for Computing Machinery, New York, NY, pp. 300-301, 16th ACM International Conference on Computing Frontiers 2019, Alghero, Italy, 30/04/19. https://doi.org/10.1145/3310273.3323427

Go Green Radio Astronomy: Approximate Computing Perspective: Opportunities and Challenges. / Gillani, Ghayoor ; Kokkeler, Andre B.J.
ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings. New York, NY: Association for Computing Machinery, 2019. p. 300-301.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Go Green Radio Astronomy

T2 - 16th ACM International Conference on Computing Frontiers 2019

AU - Gillani, Ghayoor

AU - Kokkeler, Andre B.J.

N1 - Conference code: 16

PY - 2019/4/30

Y1 - 2019/4/30

N2 - Modern radio telescopes require highly energy/power-efficient computing systems. Signal processing pipelines of such radio telescopes are dominated by accumulation based iterative processes. As the input signal received at a radio telescope is regarded as Gaussian noise, employing approximate computing looks promising. Therefore, we present opportunities and challenges offered by the approximate computing paradigm to achieve the required efficiency targets.

AB - Modern radio telescopes require highly energy/power-efficient computing systems. Signal processing pipelines of such radio telescopes are dominated by accumulation based iterative processes. As the input signal received at a radio telescope is regarded as Gaussian noise, employing approximate computing looks promising. Therefore, we present opportunities and challenges offered by the approximate computing paradigm to achieve the required efficiency targets.

KW - Approximate computing

KW - Energy efficiency

KW - Iterative workloads

KW - Power efficiency

KW - Radio astronomy

KW - 2024 OA procedure

UR - http://www.scopus.com/inward/record.url?scp=85066042099&partnerID=8YFLogxK

U2 - 10.1145/3310273.3323427

DO - 10.1145/3310273.3323427

M3 - Conference contribution

SP - 300

EP - 301

BT - ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings

PB - Association for Computing Machinery

CY - New York, NY

Y2 - 30 April 2019 through 2 May 2019

ER -

Go Green Radio Astronomy: Approximate Computing Perspective: Opportunities and Challenges

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Modeling the effects of power efficient approximate multipliers in radio astronomy correlators

Cite this