NMPO: Near-Memory Computing Profiling and Offloading

Stefano Corda; Madhurya Kumaraswamy; Ahsan Javed Awan; Roel Jordans; Akash Kumar; Henk Corporaal

doi:10.1109/DSD53832.2021.00048

NMPO: Near-Memory Computing Profiling and Offloading

Stefano Corda, Madhurya Kumaraswamy, Ahsan Javed Awan, Roel Jordans, Akash Kumar, Henk Corporaal

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

5 Citaten (Scopus)

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Real-world applications are now processing big-data sets, often bottlenecked by the data movement between the compute units and the main memory. Near-memory computing (NMC), a modern data-centric computational paradigm, can alleviate these bottlenecks, thereby improving the performance of applications. The lack of NMC system availability makes simulators the primary evaluation tool for performance estimation. However, simulators are usually time-consuming, and methods that can reduce this overhead would accelerate the early-stage design process of NMC systems. This work proposes Near-Memory computing Profiling and Offloading (NMPO), a high-level framework capable of predicting NMC offloading suitability employing an ensemble machine learning model. NMPO predicts NMC suitability with an accuracy of 85.6% and, compared to prior works, can reduce the prediction time by using hardware-dependent applications features by up to 3 order of magnitude.

Originele taal-2	Engels
Titel	Proceedings - 2021 24th Euromicro Conference on Digital System Design, DSD 2021
Redacteuren	Francesco Leporati, Salvatore Vitabile, Amund Skavhaug
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	259-267
Aantal pagina's	9
ISBN van elektronische versie	978-1-6654-2703-6
DOI's	https://doi.org/10.1109/DSD53832.2021.00048
Status	Gepubliceerd - 11 okt. 2021
Evenement	24th Euromicro conference on Digital System Design - Virtual, Online, Palermo, Italië Duur: 1 sep. 2021 → 3 sep. 2021 Congresnummer: 24

Congres

Congres	24th Euromicro conference on Digital System Design
Verkorte titel	DSD
Land/Regio	Italië
Stad	Palermo
Periode	1/09/21 → 3/09/21

Bibliografische nota

Euromicro Conference on Digital System Design 2021

Financiering

ACKNOWLEDGMENTS This work is funded by the European Commission under Marie Sklodowska-Curie Innovative Training Networks European Industrial Doctorate (Project ID: 676240). We would like to thank Gabor Nemeth from Ericsson Research for his feedback on the draft of the paper.

Financiers	Financiernummer
European Union’s Horizon Europe research and innovation programme
European Commission	676240

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10.1109/DSD53832.2021.00048

ArXiv 2106.15284v1Ingediend manuscript, 755 KB

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NeMeCo
Corporaal, H. (Project Manager), van Dalfsen, J. (Projectmedewerker), Singh, G. (Projectmedewerker), Chelini, L. (Projectmedewerker), Corda, S. (Projectmedewerker), Stuijk, S. (Projectmedewerker), Sánchez Martín, V. (Project Manager), Jordans, R. (Projectmedewerker), van der Hagen, D. (Project communicatie medewerker) & de Mol-Regels, M. (Project communicatie medewerker)
1/04/16 → 30/09/20
Project: Onderzoek direct

Best student paper award DSD 2021
Corda, S. (Ontvanger), Madhurya Kumaraswamy, M. (Ontvanger), Awan, A. J. (Ontvanger), Jordans, R. (Ontvanger) & Corporaal, H. (Ontvanger), 3 sep. 2021
Prijs: Anders › Werk, activiteit of publicatie gerelateerde prijzen (lifetime, best paper, poster etc.) › Wetenschappelijk

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Citeer dit

Corda, S., Kumaraswamy, M., Awan, A. J., Jordans, R., Kumar, A., & Corporaal, H. (2021). NMPO: Near-Memory Computing Profiling and Offloading. In F. Leporati, S. Vitabile, & A. Skavhaug (editors), Proceedings - 2021 24th Euromicro Conference on Digital System Design, DSD 2021 (blz. 259-267). Artikel 9556449 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DSD53832.2021.00048

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Corda, S, Kumaraswamy, M, Awan, AJ, Jordans, R, Kumar, A & Corporaal, H 2021, NMPO: Near-Memory Computing Profiling and Offloading. in F Leporati, S Vitabile & A Skavhaug (uitgave), Proceedings - 2021 24th Euromicro Conference on Digital System Design, DSD 2021., 9556449, Institute of Electrical and Electronics Engineers, blz. 259-267, 24th Euromicro conference on Digital System Design, Palermo, Italië, 1/09/21. https://doi.org/10.1109/DSD53832.2021.00048

NMPO: Near-Memory Computing Profiling and Offloading. / Corda, Stefano; Kumaraswamy, Madhurya; Awan, Ahsan Javed et al.
Proceedings - 2021 24th Euromicro Conference on Digital System Design, DSD 2021. uitgave / Francesco Leporati; Salvatore Vitabile; Amund Skavhaug. Institute of Electrical and Electronics Engineers, 2021. blz. 259-267 9556449.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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T2 - 24th Euromicro Conference on Digital System Design, DSD 2021

AU - Corda, Stefano

AU - Kumaraswamy, Madhurya

AU - Awan, Ahsan Javed

AU - Jordans, Roel

AU - Kumar, Akash

AU - Corporaal, Henk

N1 - Conference code: 24

PY - 2021/10/11

Y1 - 2021/10/11

N2 - Real-world applications are now processing big-data sets, often bottlenecked by the data movement between the compute units and the main memory. Near-memory computing (NMC), a modern data-centric computational paradigm, can alleviate these bottlenecks, thereby improving the performance of applications. The lack of NMC system availability makes simulators the primary evaluation tool for performance estimation. However, simulators are usually time-consuming, and methods that can reduce this overhead would accelerate the early-stage design process of NMC systems. This work proposes Near-Memory computing Profiling and Offloading (NMPO), a high-level framework capable of predicting NMC offloading suitability employing an ensemble machine learning model. NMPO predicts NMC suitability with an accuracy of 85.6% and, compared to prior works, can reduce the prediction time by using hardware-dependent applications features by up to 3 order of magnitude.

AB - Real-world applications are now processing big-data sets, often bottlenecked by the data movement between the compute units and the main memory. Near-memory computing (NMC), a modern data-centric computational paradigm, can alleviate these bottlenecks, thereby improving the performance of applications. The lack of NMC system availability makes simulators the primary evaluation tool for performance estimation. However, simulators are usually time-consuming, and methods that can reduce this overhead would accelerate the early-stage design process of NMC systems. This work proposes Near-Memory computing Profiling and Offloading (NMPO), a high-level framework capable of predicting NMC offloading suitability employing an ensemble machine learning model. NMPO predicts NMC suitability with an accuracy of 85.6% and, compared to prior works, can reduce the prediction time by using hardware-dependent applications features by up to 3 order of magnitude.

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NMPO: Near-Memory Computing Profiling and Offloading

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Congres

Bibliografische nota

Financiering

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Prijzen

Best student paper award DSD 2021

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