The mapping optimization problem in Network-on-Chip (NoC) is constraint and NP-hard, and the deterministic algorithms require considerable computation time to find an exact optimal mapping solution. Therefore, the metaheuristic algorithms (MAs) have attracted great interests of researchers. However, most MAs are designed for continuous problems and suffer from premature convergence. In this letter, a binary metaheuristic mapping algorithm (BMM) with a better exploration-exploitation balance is proposed to solve the mapping problem. The binary encoding is used to extend the MAs to the constraint problem and an adaptive strategy is introduced to combine Sine Cosine Algorithm (SCA) and Particle Swarm Algorithm (PSO). SCA is modified to explore the search space effectively, while the powerful exploitation ability of PSO is employed for the global optimum. A set of well-known applications and large-scale synthetic cores-graphs are used to test the performance of BMM. The results demonstrate that the proposed algorithm can improve the energy consumption more significantly than some other heuristic algorithms.

This paper proposes a static application mapping technique, based on integer linear programming, for non-hierarchical manycore embedded systems. Unlike previous work which was designed for hierarchical manycore SoCs, this work allows more flexible application mapping to achieve higher performance. The experimental results show the effectiveness of this work.

This paper studies mapping techniques of multiple applications on embedded many-core SoCs. The mapping techniques proposed in this paper are static which means the mapping is decided at design time. The mapping techniques take into account both inter-application and intra-application parallelism in order to fully utilize the potential parallelism of the many-core architecture. Additionally, the proposed static mapping supports dynamic application switching, which means the applications mapped onto the same cores are switched to each other at runtime. Two approaches are proposed for static mapping: one approach is based on integer linear programming and the other is based on a greedy algorithm. Experimental results show the effectiveness of the proposed techniques.

This paper studies the static mapping of multiple applications on embedded many-core SoCs. The mapping techniques proposed in this paper take into account both inter-application and intra-application parallelism in order to fully utilize the potential parallelism of the many-core architecture. Two approaches are proposed for static mapping: one approach is based on integer linear programming and the other is based on a greedy algorithm. Experiments show the effectiveness of the proposed techniques.