2016
DOI: 10.1109/tpds.2015.2457444
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Performance and Energy Aware Inhomogeneous 3D Networks-on-Chip Architecture Generation

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Cited by 32 publications
(28 citation statements)
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“…In order to evaluate the performance of the proposed bypassing technique in 3D NoCs and to facilitate correlation with existing work, an extended version of W orm sim, a cycleaccurate NoC simulator [3] is used. Our extended simulator employs wormhole packet switching flow control to accurately simulate 3D NoCs with any configuration of 3D and 2D routers.…”
Section: Discussionmentioning
confidence: 99%
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“…In order to evaluate the performance of the proposed bypassing technique in 3D NoCs and to facilitate correlation with existing work, an extended version of W orm sim, a cycleaccurate NoC simulator [3] is used. Our extended simulator employs wormhole packet switching flow control to accurately simulate 3D NoCs with any configuration of 3D and 2D routers.…”
Section: Discussionmentioning
confidence: 99%
“…To analyse the performance benefits of inhomogeneous 3D NoC architectures implemented with the proposed SlideAcross router, Branch-and-Bound [3] mapping algorithm algorithm is used to map the applications to various inhomogeneous architectures for comparison. For inhomogeneous 3D NoCs with bypass techniques (a.k.a SlideAcross), we replace the conventional 2D routers by the SlideAcross routers.…”
Section: Discussionmentioning
confidence: 99%
“…Applications in such 3D NoCs are not optimized, as communication bandwidth and performance constraints of the applications were not considered in the architecture generation. To resolve this, a systematic approach for generating inhomogeneous 3D NoC architectures where the TSV and buffer utilization of the given application are exploited is proposed in [3]. Though inhomogeneous 3D NoC architectures reduce the number of power (up to 67%) and area hungry 3D routers, as well as the number of TSVs, they inhibit the total performance of the NoC.…”
Section: D Network-on-chipmentioning
confidence: 99%
“…Each piece of these devices is designed based on the predefined tasks of a specific board. Since these devices are designed to focus on specific sort of applications, sometimes heterogeneous devices consist different set and types of cores [1][2][3][4][5][6], and that add more challenges in order to measure performance or find the best suitable simulation technique. High performance computing (HPC) community started the journey of these accelerators when they used Graphics Processing Units (GPUs) as accelerators for general purpose computations [7] and that what derived the term of General Purpose Computing on GPUs (GPGPU) [8].…”
Section: Introductionmentioning
confidence: 99%