Approximation scheme for restricted discrete gate sizing targeting delay minimization
Journal of Combinatorial Optimization
Gate sizing and device technology selection algorithms for high-performance industrial designs
Proceedings of the International Conference on Computer-Aided Design
The ISPD-2012 discrete cell sizing contest and benchmark suite
Proceedings of the 2012 ACM international symposium on International Symposium on Physical Design
An efficient algorithm for library-based cell-type selection in high-performance low-power designs
Proceedings of the International Conference on Computer-Aided Design
Sensitivity-guided metaheuristics for accurate discrete gate sizing
Proceedings of the International Conference on Computer-Aided Design
An improved benchmark suite for the ISPD-2013 discrete cell sizing contest
Proceedings of the 2013 ACM international symposium on International symposium on physical design
High-performance gate sizing with a signoff timer
Proceedings of the International Conference on Computer-Aided Design
Multivariate convex regression with adaptive partitioning
The Journal of Machine Learning Research
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Convex-optimization techniques are very popular in the very large-scale-integration design society due to their guaranteed convergence to a global optimal point. The table data need to be fitted into convex forms to be used in the convex optimization problems. Fitting the tables into polynomials, which are analytically convex under logarithmic transformation, may suffer from the excessive fitting errors as the fitting problem is nonconvex. In this paper, we propose to directly adjust the lookup-table values into a numerically convex lookup table without any explicit analytical form. We show that numerically "convexifying" the lookup-table data with minimum perturbation can be formulated as a convex semidefinite optimization problem, and hence, optimality can be reached in polynomial time. We also propose three algorithms to make the table data smooth to enable faster convergence of the convex optimizer. Results from extensive experiments on industrial cell libraries demonstrate 9.6 improvement in fitting error over a well-developed polynomial-fitting procedure. We illustrate the effectiveness of this model in a convex optimization problem by providing results for using our model in the optimal gate sizing of standard cells. We observe a 5.07% improvement in the delay of International Symposium on Circuits and Systems (ISCAS) benchmark circuits over the polynomial-fitting procedure.