Force-directed scheduling in automatic data path synthesis
DAC '87 Proceedings of the 24th ACM/IEEE Design Automation Conference
Genetic algorithms and instruction scheduling
MICRO 24 Proceedings of the 24th annual international symposium on Microarchitecture
High-level synthesis scheduling and allocation using genetic algorithms
ASP-DAC '95 Proceedings of the 1995 Asia and South Pacific Design Automation Conference
Synthesis and Optimization of Digital Circuits
Synthesis and Optimization of Digital Circuits
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A scheduling algorithm for optimization and early planning in high-level synthesis
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Instruction scheduling using MAX-MIN ant system optimization
GLSVLSI '05 Proceedings of the 15th ACM Great Lakes symposium on VLSI
Time-constrained scheduling of large pipelined datapaths
Journal of Systems Architecture: the EUROMICRO Journal
Timetable Scheduling Using Particle Swarm Optimization
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Particle Swarm Algorithm for Tasks Scheduling in Distributed Heterogeneous System
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
International Journal of Organizational and Collective Intelligence
VLSI Design - Special issue on New Algorithmic Techniques for Complex EDA Problems
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Instruction scheduling is an optimization phase aimed at balancing the performance-cost tradeoffs of the design of digital systems. In this paper, a formal framework is tailored in particular to find an optimal solution to the resource-constrained instruction scheduling problem in high-level synthesis. The scheduling problem is formulated as a discrete optimization problem and an efficient population-based search technique; particle swarm optimization (PSO) is incorporated for efficient pruning of the solution space. As PSO has proven to be successful in many applications in continuous optimization problems, the main contribution of this paper is to propose a new hybrid algorithm that combines PSO with the traditional list scheduling algorithm to solve the discrete problem of instruction scheduling. The performance of the proposed algorithms is evaluated on a set of HLS benchmarks, and the experimental results demonstrate that the proposed algorithm outperforms other scheduling metaheuristics and is a promising alternative for obtaining near optimal solutions to NP-complete scheduling problem instances.