Understanding and optimizing asynchronous low-precision stochastic gradient descent

Stochastic gradient descent (SGD) is one of the most popular numerical algorithms used in machine learning and other domains. Since this is likely to continue for the foreseeable future, it is important to study techniques that can make it run fast on parallel hardware. In this paper, we provide the...

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Veröffentlicht in:	2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA) S. 561 - 574
Hauptverfasser:	De Sa, Christopher, Feldman, Matthew, Re, Christopher, Olukotun, Kunle
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 01.06.2017
Schlagworte:	Algorithm design and analysis asynchrony Computational modeling FPGA Hardware Logistics low precision multicore Optimization Stochastic gradient descent Stochastic processes Throughput
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