Due to the ever-increasing complexity of System on Chip (SoC) design, and non-efficiency of electric bus to exchange data between IP cores in Giga scale, the Network on Chip (NoC) is presented with more flexible, scalable and reliable infra-structure. As mapping of IP cores on a given platform is one of three aspects of NoC design, with the focus on tile-based NoC architecture, we have introduced a heuristic method for mapping cores on mesh platform. Onyx 1 algorithm is a method with less complexity, and it minimizes hop count between IP cores, leading to improving energy consumption and other performance parameters. We have used this method with two real applications, i.e. VOPD 2 , and MPEG-4 and compared it with some existing algorithms. The results show that our developed method is more efficient. Keywords: Network on Chip, topology, mapping, core graph, NoC architecture graph, communication cost, bandwidth constraints Classification: Integrated circuits References[1] S. Kumar, A. Jantsch, J. P. Sonioen, M. Forsell, M. Millberg, J. Oeberg, K. Tiensirja, and A. Hemani, "A network on chip architecture and design methodology," IEEE Symp.

Abstract:Mapping of IP cores on a given platform is one of the three aspects of Network-on-Chip design. Mapping priority of IP cores is mostly based on a single communication in previously proposed algorithms. In this paper we present Chain-Mapping (CHMAP), as an algorithm for mapping cores onto a mesh-based Network-on-Chip architecture. The main aim of the algorithm is to produce chains of connected cores in order to introduce a new method to prioritize IP core which helps us to have more efficient mapping. Proposed algorithm and previous researches were compared on two real applications, i.e. Video object plan decoder (VOPD) and MPEG-4 and results were reported.

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We present the use of DEVS and Cell-DEVS formalisms to model different approaches in networking applications. We discuss various applications of discrete event system specifications for modeling and simulation of Wireless networks and Wireless Sensor Networks (WSN). We discuss how to use the Cell-DEVS formalism to model a WSN for investigating on stochastic properties of malware propagation and the intrinsic characteristic of WSN. We also discuss the use of DEVS to model a cellular network including a wide geographical area, various Cells and varied User Equipment. Finally, we discuss how to use the cell DEVS formalism to model mobile networks, and how the Cell-DEVS formalism can be used to track mobile user movement in a covered area. The latter model tries to find out the number of Base Stations which cover a mobile user in different location of an area and how to improve QoS based on different configurations (in particular for the UEs near the cell borders).

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