Graph Analytics Accelerators for Cognitive Systems

Hardware accelerators are known to be performance and power efficient. This article focuses on accelerator design for graph analytics applications, which are commonly used kernels for cognitive systems. The authors propose a templatized architecture that is specifically optimized for vertex-centric...

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Veröffentlicht in:	IEEE MICRO 2017-01, Vol.37 (1), p.42-51
Hauptverfasser:	Ozdal, Muhammet Mustafa, Yesil, Serif, Taemin Kim, Ayupov, Andrey, Greth, John, Burns, Steven, Ozturk, Ozcan
Format:	Artikel
Sprache:	eng
Schlagworte:	Accelerators Cognitive models Computational modeling Computer architecture Computer graphics Computer memory Customization Data structures Design Energy conversion efficiency Graph analytics Kernels parallel architectures Parallel processing Random access memory Scheduling special-purpose and application-based systems Synchronization
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