Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/593468
Title: An Efficient Network-on-Chip Yield Estimation Approach Based on Gibbs Sampling
Authors: Fan Lan;Yun Pan;Kwang-Ting Tim Cheng
subject: Network-on-Chip|Gibbs Sampling|Yield
Year: 2016
Publisher: IEEE
Abstract: A network-on-chip (NoC), a redundancy-rich and thus relatively robust system-chip, is still vulnerable to defects due to its large-scale integration. Thus, it is desirable to analyze the NoC yield in an early design phase. A Monte Carlo (MC) approach was proposed for the NoC yield analysis at the system level; however, it is inefficient due to the requirement of a large number of simulation runs. In this paper, we propose a Gibbs sampling approach, which can efficiently generate failed NoC instances as simulation samples, for yield estimation. This approach significantly reduces the number of required simulation runs for obtaining an accurate yield estimation. Implementation issues, such as initial sample selection, calculation of conditional distributions, and stop criterion, to customize Gibbs sampling for the NoC yield analysis are discussed. Potential optimization opportunities to further improve Gibbs sampling's efficiency are also explored. Compared to the MC approach, our experimental results show that the proposed approach can reduce the simulation runtime by 5×-100× for a high-yield NoC (a failure rate at 10-2 -10-5 ), while achieving the same level of accuracy for yield estimation.
URI: http://localhost/handle/Hannan/175385
http://localhost/handle/Hannan/593468
ISSN: 0278-0070
1937-4151
volume: 35
issue: 3
Appears in Collections:2016

Files in This Item:
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Title: An Efficient Network-on-Chip Yield Estimation Approach Based on Gibbs Sampling
Authors: Fan Lan;Yun Pan;Kwang-Ting Tim Cheng
subject: Network-on-Chip|Gibbs Sampling|Yield
Year: 2016
Publisher: IEEE
Abstract: A network-on-chip (NoC), a redundancy-rich and thus relatively robust system-chip, is still vulnerable to defects due to its large-scale integration. Thus, it is desirable to analyze the NoC yield in an early design phase. A Monte Carlo (MC) approach was proposed for the NoC yield analysis at the system level; however, it is inefficient due to the requirement of a large number of simulation runs. In this paper, we propose a Gibbs sampling approach, which can efficiently generate failed NoC instances as simulation samples, for yield estimation. This approach significantly reduces the number of required simulation runs for obtaining an accurate yield estimation. Implementation issues, such as initial sample selection, calculation of conditional distributions, and stop criterion, to customize Gibbs sampling for the NoC yield analysis are discussed. Potential optimization opportunities to further improve Gibbs sampling's efficiency are also explored. Compared to the MC approach, our experimental results show that the proposed approach can reduce the simulation runtime by 5×-100× for a high-yield NoC (a failure rate at 10-2 -10-5 ), while achieving the same level of accuracy for yield estimation.
URI: http://localhost/handle/Hannan/175385
http://localhost/handle/Hannan/593468
ISSN: 0278-0070
1937-4151
volume: 35
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7229262.pdf2.86 MBAdobe PDFThumbnail
Preview File
Title: An Efficient Network-on-Chip Yield Estimation Approach Based on Gibbs Sampling
Authors: Fan Lan;Yun Pan;Kwang-Ting Tim Cheng
subject: Network-on-Chip|Gibbs Sampling|Yield
Year: 2016
Publisher: IEEE
Abstract: A network-on-chip (NoC), a redundancy-rich and thus relatively robust system-chip, is still vulnerable to defects due to its large-scale integration. Thus, it is desirable to analyze the NoC yield in an early design phase. A Monte Carlo (MC) approach was proposed for the NoC yield analysis at the system level; however, it is inefficient due to the requirement of a large number of simulation runs. In this paper, we propose a Gibbs sampling approach, which can efficiently generate failed NoC instances as simulation samples, for yield estimation. This approach significantly reduces the number of required simulation runs for obtaining an accurate yield estimation. Implementation issues, such as initial sample selection, calculation of conditional distributions, and stop criterion, to customize Gibbs sampling for the NoC yield analysis are discussed. Potential optimization opportunities to further improve Gibbs sampling's efficiency are also explored. Compared to the MC approach, our experimental results show that the proposed approach can reduce the simulation runtime by 5×-100× for a high-yield NoC (a failure rate at 10-2 -10-5 ), while achieving the same level of accuracy for yield estimation.
URI: http://localhost/handle/Hannan/175385
http://localhost/handle/Hannan/593468
ISSN: 0278-0070
1937-4151
volume: 35
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7229262.pdf2.86 MBAdobe PDFThumbnail
Preview File