Application Synthesis for MPSoCs Implementation Using Multiobjective Optimization
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Optimal application mapping on NoC infrastructure using NSGA-II and microGA
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
Evolutionary IP assignment for efficient NoC-based system design using multi-objective optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Efficient mapping of an image processing application for a network-on-chip based implementation
International Journal of High Performance Systems Architecture
Journal of Systems Architecture: the EUROMICRO Journal
Expert Systems with Applications: An International Journal
Multi-objective artificial immune algorithm for security-constrained multi-application NoC mapping
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
A multi-objective mapping strategy for application specific emesh network-on-chip (noc)
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
A survey on application mapping strategies for Network-on-Chip design
Journal of Systems Architecture: the EUROMICRO Journal
Developing Domain-Knowledge Evolutionary Algorithms for Network-on-Chip Application Mapping
Microprocessors & Microsystems
Proceedings of the 21st International conference on Real-Time Networks and Systems
Hi-index | 0.00 |
Network on Chip (NoC) is a new paradigm for design core based System on Chip (SoC) which supports high degree of reusability and provides increase computation power. This paper addresses the problem of topological mapping of intellectual properties (IPs) on the tiles of a mesh-based NoC in two systematic steps using multiobjective evolutionary algorithm. The main objective is to obtain the pareto mappings that minimized the energy consumption (Computational and Communicational) and link bandwidth requirement under performance constraints. The evaluation performed on three randomly generated benchmarks and a real application ( M-JPEG encoder) to conform the efficiency, accuracy and scalability of the proposed approach. Our proposed approach saves up to (15% - 20%) of energy and bandwidth requirements in comparison to the existing approaches.