Retiming synchronous circuitry with imprecise delays
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
Exploiting intellectual properties with imprecise design costs for system-on-chip synthesis
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A three-phase integrated model for product configuration change problems
Expert Systems with Applications: An International Journal
A fuzzy optimization approach for variation aware power minimization during gate sizing
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Design exploration framework under impreciseness based on register-constrained inclusion scheduling
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
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The knowledge about available resources during high-level synthesis is usually imprecise. Previous methods seem to have ignored this fact, possibly to avoid an increase in the, already high, computational complexity. In this paper an approach based on so called "possibilistic" programming, a kind of fuzzy mathematical programming, is presented. Using this method we can improve existing mathematical programming methods for the architectural synthesis while keeping their good properties. Not only architectures which optimize the most possible value of the cost function can be generated, but more importantly, also the tradeoff between this goal and reducing the probability of obtaining worse solution and enhancing probability of obtaining a better solution is controlled. At the same time, an increase in the computational complexity of the algorithms is avoided. To show the validity of the approach an application to simultaneous scheduling, selection and allocation of functional units is described. The approach has been implemented in a system called FOAS. Experimental results confirm the advantages of the proposed methodology.