Paper Info
Title
Hardware Supported Adaptive Data Collection for Networks on Chip
Abstract
Managing future many-core architectures with hundreds of cores, running multiple applications in parallel, is very challenging. One of the major reasons is the communication overhead required to handle such a large system. Distributed management is proposed to reduce this overhead. The architecture is divided into regions which are managed separately. The instance managing the region and the applications running within the regions need to collect data for various reasons from time to time, e.g., to collect data for proper mapping decision, to synchronize tasks or to aggregate computation results. In this work, we propose and investigate different strategies for adaptive data collection in meshed Networks on Chip. The mechanisms can be used to collect data within regions, which are defined during run-time in respect of size and position. The mechanisms are investigated while considering delay, NoC utilization and implementation costs. The results show that the selection of the used mechanism depends on the requirements. Synthesis results compare area overhead, timing impact and energy consumption.
Year
DOI
Venue
2013
10.1109/IPDPSW.2013.124
IPDPS Workshops
Keywords
Field
DocType
implementation cost,different strategy,aggregate computation result,used mechanism,hardware supported adaptive data,energy consumption,noc utilization,adaptive data collection,large system,future many-core architecture,area overhead,routing,hamilton cycle,data collection,network on chip,hardware,aggregation,computer architecture
Data collection,Architecture,Synchronization,Computer science,Network on a chip,Energy consumption,Distributed management,Distributed computing,Computation,Embedded system
Conference
Citations 
PageRank 
References 
1
0.36
18
Authors
8
Name
Order
Citations
PageRank
Jan Heisswolf1515.67
Andreas Weichslgartner2527.16
Aurang Zaib3265.74
Ralf Konig4211.49
Thomas Wild512425.65
Andreas Herkersdorf670388.32
Jurgen Teich730622.61
Jürgen Becker81894259.42