Modeling NoC architectures by means of deterministic and stochastic petri nets
Embedded Computer Systems: Architectures, Modeling, and Simulation: 5th …, 2005•Springer
The design of appropriate communication architectures for complex Systems-on-Chip (SoC)
is a challenging task. One promising alternative to solve these problems are Networks-on-
Chip (NoCs). Recently, the application of deterministic and stochastic Petri-Nets (DSPNs) to
model on-chip communication has been proven to be an attractive method to evaluate and
explore different communication aspects. In this contribution the modeling of basic NoC
communication scenarios featuring different processor cores, network topologies and …
is a challenging task. One promising alternative to solve these problems are Networks-on-
Chip (NoCs). Recently, the application of deterministic and stochastic Petri-Nets (DSPNs) to
model on-chip communication has been proven to be an attractive method to evaluate and
explore different communication aspects. In this contribution the modeling of basic NoC
communication scenarios featuring different processor cores, network topologies and …
Abstract
The design of appropriate communication architectures for complex Systems-on-Chip (SoC) is a challenging task. One promising alternative to solve these problems are Networks-on-Chip (NoCs). Recently, the application of deterministic and stochastic Petri-Nets (DSPNs) to model on-chip communication has been proven to be an attractive method to evaluate and explore different communication aspects. In this contribution the modeling of basic NoC communication scenarios featuring different processor cores, network topologies and communication schemes is presented. In order to provide a test bed for the verification of modeling results a state-of-the-art FPGA-platform has been utilized. This platform allows to instantiate a soft-core processor network which can be adapted in terms of communication network topologies and communication schemes. It will be shown that DSPN modeling yields good prediction results at low modeling effort. Different DSPN modeling aspects in terms of accuracy and computational effort are discussed.
Springer
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