Statistical physics approaches for network-on-chip traffic characterization

In order to face the growing complexity of embedded applications, we aim to build highly efficient Network-on-Chip (NoC) architectures which can connect in a scalable manner various computational modules of the platform. For such networked platforms, it is increasingly important to accurately model the traffic characteristics as this is intimately related to our ability to determine the optimal buffer size at various routers in the network and thus provide analytical metrics for various power-performance trade-offs. In this paper, we show that the main limitations of queueing theory and Markov chain approaches to solving the buffer sizing problem can be overcome by adopting a statistical physics approach to probability density characterization which incorporates the power law distribution, correlations, and scaling properties exhibited within an NoC architecture due to various network transactions. As experimental results show, this new approach represents a breakthrough in accurate traffic modeling under non-equilibrium conditions. As such, our results can be directly used to solve the buffer sizing problem for multiprocessor systems where communication happens via the NoC approach.