Proceedings of the 6th international workshop on Hardware/software codesign
Hardware/software partitioning with integrated hardware design space exploration
Proceedings of the conference on Design, automation and test in Europe
Proceedings of the ninth international symposium on Hardware/software codesign
Hardware/Software CO-Design for Data Flow Dominated Embedded Systems
Hardware/Software CO-Design for Data Flow Dominated Embedded Systems
Machine Learning
Introduction to Algorithms
Hardware-Software partitioning and pipelined scheduling of transformative applications
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Codesign of Embedded Systems: Status and Trends
IEEE Design & Test
Formal Models for Embedded System Design
IEEE Design & Test
Multi-objective optimization of interconnect geometry
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on system-level interconnect prediction (SLIP)
Hardware Software Partitioning Using Genetic Algorithm
VLSID '97 Proceedings of the Tenth International Conference on VLSI Design: VLSI in Multimedia Applications
HW/SW Codesign Incorporating Edge Delays Using Dynamic Programming
DSD '03 Proceedings of the Euromicro Symposium on Digital Systems Design
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
GPH: A group-based partitioning scheme for reducing total power consumption of parallel buses
Microprocessors & Microsystems
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Hardware/Software partitioning is one of the most important issues of codesign of embedded systems, since the costs and delays of the final results of a design will strongly depend on partitioning. We present an algorithm based on Particle Swarm Optimization to perform the hardware/software partitioning of a given task graph for minimum cost subject to timing constraint. By novel evolving strategy, we enhance the efficiency and result's quality of our partitioning algorithm in an acceptable run-time. Also, we compare our results with those of Genetic Algorithm on different task graphs. Experimental results show the algorithm's effectiveness in achieving the optimal solution of the HW/SW partitioning problem even in large task graphs.