NMPO: Near-Memory Computing Profiling and Offloading.

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

Abstract

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.

Details

OriginalspracheEnglisch
TitelProceedings - 2021 24th Euromicro Conference on Digital System Design, DSD 2021
Redakteure/-innenFrancesco Leporati, Salvatore Vitabile, Amund Skavhaug
Seiten259-267
Seitenumfang9
ISBN (elektronisch)978-1-6654-2703-6
PublikationsstatusVeröffentlicht - 2021
Peer-Review-StatusJa

Publikationsreihe

ReiheEuromicro Symposium on Digital System Design (DSD)
ISSN2639-3859

Externe IDs

Scopus 85125803813
Mendeley e730caec-98de-30eb-926b-fa354a09ce9a

Schlagworte