Scale-out acceleration for machine learning

The growing scale and complexity of Machine Learning (ML) algorithms has resulted in prevalent use of distributed general-purpose systems. In a rather disjoint effort, the community is focusing mostly on high performance single-node accelerators for learning. This work bridges these two paradigms an...

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Veröffentlicht in:	MICRO-50 : the 50th annual IEEE/ACM International Symposium on Microarchitecture : proceedings : October 14-18, 2017, Cambridge, MA S. 367 - 381
Hauptverfasser:	Park, Jongse, Sharma, Hardik, Mahajan, Divya, Kim, Joon Kyung, Olds, Preston, Esmaeilzadeh, Hadi
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	New York, NY, USA ACM 14.10.2017
Schriftenreihe:	ACM Conferences
Schlagworte:	Acceleration Accelerator cloud Computer systems organization > Architectures > Distributed architectures > Cloud computing Computing methodologies > Machine learning distributed Field programmable gate arrays Hardware Hardware > Integrated circuits > Reconfigurable logic and FPGAs > Hardware accelerators Machine learning Machine learning algorithms scale-out Software algorithms System software cloud accelerator distributed scale-out machine learning
ISBN:	1450349528, 9781450349529
ISSN:	2379-3155
Online-Zugang:	Volltext
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