DeftNN addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission

Deep neural networks (DNNs) are key computational building blocks for emerging classes of web services that interact in real time with users via voice, images and video inputs. Although GPUs have gained popularity as a key accelerator platform for deep learning workloads, the increasing demand for D...

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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. 786 - 799
Hauptverfasser:	Hill, Parker, Jain, Animesh, Hill, Mason, Zamirai, Babak, Hsu, Chang-Hong, Laurenzano, Michael A., Mahlke, Scott, Tang, Lingjia, Mars, Jason
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	New York, NY, USA ACM 14.10.2017
Schriftenreihe:	ACM Conferences
Schlagworte:	Bandwidth Computer systems organization > Architectures > Parallel architectures > Single instruction, multiple data Computing methodologies > Machine learning > Machine learning approaches > Neural networks Deep Neural Networks General and reference > Cross-computing tools and techniques > Performance GPU Architecture Graphics processing units Hardware Memory Bandwidth Memory management Optimization Performance Optimization Synapses System-on-chip memory bandwidth GPU architecture performance optimization deep neural networks
ISBN:	1450349528, 9781450349529
ISSN:	2379-3155
Online-Zugang:	Volltext
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