Optimal orientations of cells in slicing floorplan designs
Information and Control
Data structures and network algorithms
Data structures and network algorithms
AVL-trees for localized search
Information and Control
Combinatorial algorithms for integrated circuit layout
Combinatorial algorithms for integrated circuit layout
Area minimization for hierarchical floorplans
ICCAD '94 Proceedings of the 1994 IEEE/ACM international conference on Computer-aided design
Transforming an arbitrary floorplan into a sliceable one
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
A new area and shape function estimation technique for VLSI layouts
DAC '88 Proceedings of the 25th ACM/IEEE Design Automation Conference
Lower bounds for algebraic computation trees
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
DAC '82 Proceedings of the 19th Design Automation Conference
Area minimization for floorplans
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A performance-driven IC/MCM placement algorithm featuring explicit design space exploration
ACM Transactions on Design Automation of Electronic Systems (TODAES)
TINA: analog placement using enumerative techniques capable of optimizing both area and net length
EURO-DAC '96/EURO-VHDL '96 Proceedings of the conference on European design automation
Physical Design of CMOS Chips in Six Easy Steps
SOFSEM '00 Proceedings of the 27th Conference on Current Trends in Theory and Practice of Informatics
Algorithms and theory of computation handbook
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The traditional algorithm of Stockmeyer for area minimization of slicing floorplans has time (and space) complexity O(n^2) in the worst case, or O(n\log n) for balanced slicing. For more than a decade, it is considered the best possible. In this paper, we present a new algorithm of worst-case time (and space) complexity O(n\log n), where n is the total number of realizations for the basic blocks, regardless whether the slicing is balanced or not. We also prove \Omega(n\log n) is the lower bound on the time complexity of any area minimization algorithm. Therefore, the new algorithm not only finds the optimal realization, but also has an optimal running time.