2012 IEEE/ACM Sixth International Symposium on Networks-on-Chip 2012
DOI: 10.1109/nocs.2012.18
|View full text |Cite
|
Sign up to set email alerts
|

Reservation-based Network-on-Chip Timing Models for Large-scale Architectural Simulation

Abstract: Abstract-Architectural simulation is an essential tool when it comes to evaluating the design of future many-core chips. However, reproducing all the components of such complex systems precisely would require unreasonable amounts of computing power. Hence, a trade off between accuracy and compute time is needed. For this reason most state-of-the-art tools do not have accurate models for the networks-on-chip, and rely on timing models that permit fast-simulation. Generally, these models are very simplistic and … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2013
2013
2014
2014

Publication Types

Select...
2
1

Relationship

1
2

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 36 publications
0
1
0
Order By: Relevance
“…We also added a fault-injection model for evaluating the overhead introduced by the Double Execution mechanism, which was also modeled with the FDU. Finally, we extended the platform in order to pass from a cluster-based view of the target teradevice system, to a many-nodes-per-chip one, by realizing a communication mechanism via the host shared memory, considering appropriate timing model [27]. IX.…”
Section: The Common Evaluation Platformmentioning
confidence: 99%
“…We also added a fault-injection model for evaluating the overhead introduced by the Double Execution mechanism, which was also modeled with the FDU. Finally, we extended the platform in order to pass from a cluster-based view of the target teradevice system, to a many-nodes-per-chip one, by realizing a communication mechanism via the host shared memory, considering appropriate timing model [27]. IX.…”
Section: The Common Evaluation Platformmentioning
confidence: 99%