Evaluation of energy and buffer aware application mapping for networks-on-chip

Çelik, Coşkun; Bazlamaçcı, Cüneyt F.

doi:10.1016/j.micpro.2014.03.009

Cited by 6 publications

(2 citation statements)

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“…Application characterization graph (APCG) is a directed graph g = G(V, E) in which each vertex v i e V refers to a core and each directed edge e i,j [ E refers to the communication from the vertex v i to vertex v j . For each e i , j , some quantities are introduced as the characteristics of the edges of the graph (Çelik and Bazlamaçcı, 2014;Huang et al, 2013).…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…E there is also the number of edges. In fact, different maps pursue different goals, and these goals can include minimizing the delay, the energy consumption, the communication cost or the required bandwidth (Çelik and Bazlamaçcı, 2014). PSO is considered as a crowd-based dummy intelligence procedure inducted by the social treatment of a bird swarm (Abedinia et al, 2017;Ghadimi et al, 2017;Mir et al, 2018), in which each particle is accountable for exploring the compatibility of each particle (Aghajani and Ghadimi, 2018;Ebadi and Jafari Navimipour, 2019;Ghadimi et al, 2013).…”

Section: Ijpcc 162mentioning

confidence: 99%

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Tasks mapping in the network on a chip using an improved optimization algorithm

Darbandi¹,

Ramtin

Sharafi

2020

IJPCC

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Purpose A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose. Design/methodology/approach In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC. Findings The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm. Originality/value As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

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Section: Proposed Algorithmmentioning

confidence: 99%