Performance trade-off analysis of analog circuits by normal-boundary intersection
Proceedings of the 40th annual Design Automation Conference
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Supervised design space exploration by compositional approximation of Pareto sets
Proceedings of the 48th Design Automation Conference
Efficient design space exploration for component-based system design
Proceedings of the International Conference on Computer-Aided Design
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The convex weighted-sum method for multi-objective optimization has the desirable property of not worsening the difficulty of the optimization problem, but can lead to very nonuniform sampling. This paper explains the relationship between the weights and the partial derivatives of the tradeoff surface, and shows how to use it to choose the right weights and uniformly sample largely convex tradeoff surfaces. It proposes a novel method, Derivative Pursuit (DP), that iteratively refines a simplicial approximation of the tradeoff surface by using partial derivative information to guide the weights generation. We demonstrate the improvements offered by DP on both synthetic and circuit test cases, including a 22 nm SRAM bitcell design problem with strict read and write yield constraints, and power and performance objectives.