Fast and low overhead architectural transaction level modelling for large-scale network-on-chip simulation

2012 ◽  
Vol 6 (6) ◽  
pp. 384-395 ◽  
Author(s):  
M. Hosseinabady ◽  
J.L. Nunez-Yanez
Keyword(s):  
Large Scale ◽  
Network On Chip ◽  
Large Scale Network ◽  
Scale Network ◽  
Transaction Level ◽  
On Chip

A Deterministic-Path Routing Algorithm for Tolerating Many Faults on Very-Large-Scale Network-on-Chip

2021 ◽  
Vol 26 (1) ◽  
pp. 1-26
Author(s):  
Ying Zhang ◽  
Xinpeng Hong ◽  
Zhongsheng Chen ◽  
Zebo Peng ◽  
Jianhui Jiang
Keyword(s):  
Large Scale ◽  
Routing Algorithm ◽  
Network On Chip ◽  
Large Scale Network ◽  
Scale Network ◽  
On Chip

scholarly journals Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yin Zhen Tei ◽  
Yuan Wen Hau ◽  
N. Shaikh-Husin ◽  
M. N. Marsono
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Pareto Front ◽  
Mapping Technique ◽  
Network Partitioning ◽  
Application Mapping ◽  
Knowledge Based ◽  
Large Scale Network ◽  
Scale Network ◽  
On Chip

This paper proposes a multiobjective application mapping technique targeted for large-scale network-on-chip (NoC). As the number of intellectual property (IP) cores in multiprocessor system-on-chip (MPSoC) increases, NoC application mapping to find optimum core-to-topology mapping becomes more challenging. Besides, the conflicting cost and performance trade-off makes multiobjective application mapping techniques even more complex. This paper proposes an application mapping technique that incorporates domain knowledge into genetic algorithm (GA). The initial population of GA is initialized with network partitioning (NP) while the crossover operator is guided with knowledge on communication demands. NP reduces the large-scale application mapping complexity and provides GA with a potential mapping search space. The proposed genetic operator is compared with state-of-the-art genetic operators in terms of solution quality. In this work, multiobjective optimization of energy and thermal-balance is considered. Through simulation, knowledge-based initial mapping shows significant improvement in Pareto front compared to random initial mapping that is widely used. The proposed knowledge-based crossover also shows better Pareto front compared to state-of-the-art knowledge-based crossover.

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