Go green radio astronomy: Approximate Computing Perspective: Opportunities and Challenges: POSTER
Pages 300 - 301
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.
References
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B.F. Burke and F. Graham-Smith. 2010. An Introduction to Radio Astronomy. Cambridge University Press, Third Edition.
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G.A. Gillani et al. 2018. SquASH: Approximate Square-Accumulate With Self-Healing. IEEE Access 6 (2018), 49112--49128.
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V.K. Chippa et al. 2013. Analysis and characterization of inherent application resilience for approximate computing. In Proceedings of the 50th DAC. ACM, 113.
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G.A. Gillani and A.B.J. Kokkeler. 2017. Improving Error Resilience Analysis Methodology of Iterative Workloads for Approximate Computing. In Proceedings of the Computing Frontiers Conference. ACM, 374--379.
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G.A. Gillani, A. Krapukhin, and A.B.J. Kokkeler. 2019. Energy-Efficient Approximate Least Squares Accelerator. In Computing Frontiers Conference. ACM.
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Index Terms
- Go green radio astronomy: Approximate Computing Perspective: Opportunities and Challenges: POSTER
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Published In
April 2019
414 pages
ISBN:9781450366854
DOI:10.1145/3310273
- General Chairs:
- Francesca Palumbo,
- Michela Becchi,
- Program Chairs:
- Martin Schulz,
- Kento Sato
Copyright © 2019 Owner/Author.
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Published: 30 April 2019
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- Dutch Ministry of EL&I
- Province of Drenthe
- Netherlands Organization for Scientific Research (NWO)
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