0000000000704309
AUTHOR
Ciprian Radu
Domain-Knowledge Optimized Simulated Annealing for Network-on-Chip Application Mapping
Network-on-Chip architectures are scalable on-chip interconnection networks. They replace the inefficient shared buses and are suitable for multicore and manycore systems. This paper presents an Optimized Simulated Annealing (OSA) algorithm for the Network-on-Chip application mapping problem. With OSA, the cores are implicitly and dynamically clustered using knowledge about communication demands. We show that OSA is a more feasible Simulated Annealing approach to NoC application mapping by comparing it with a general Simulated Annealing algorithm and a Branch and Bound algorithm, too. Using real applications we show that OSA is significantly faster than a general Simulated Annealing, withou…
A stigmergic approach for social interaction design in collaboration engineering
The increasing number of available collaborative tools and their extensive use in many organizational activities has constantly raised the complexity of collaboration engineering. It presumes the design of group decision processes, supported by a wide-range of groupware tools, in an ill-structured, dynamic, and open environment. As many of these processes are recurring by nature, the development of a shared repository to store the collective knowledge and experiences of group decision process designs became a core research topic of collaboration engineering in last few years. The paper presents a human-computer interaction engineering approach to design a software prototype that provides pe…
Developing Domain-Knowledge Evolutionary Algorithms for Network-on-Chip Application Mapping
This paper addresses the Network-on-Chip (NoC) application mapping problem. This is an NP-hard problem that deals with the optimal topological placement of Intellectual Property cores onto the NoC tiles. Network-on-Chip application mapping Evolutionary Algorithms are developed, evaluated and optimized for minimizing the NoC communication energy. Two crossover and one mutation operators are proposed. It is analyzed how each optimization algorithm performs with every genetic operator, in terms of solution quality and convergence speed. Our proposed operators are compared with state-of-the-art genetic operators for permutation problems. Finally, the problem is approached in a multi-objective w…