Analytical placement: A linear or a quadratic objective function?
DAC '91 Proceedings of the 28th ACM/IEEE Design Automation Conference
Efficient and effective placement for very large circuits
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
A min-cut placement algorithm for general cell assemblies based on a graph representation
DAC '79 Proceedings of the 16th Design Automation Conference
Towards optimal circuit layout using advanced search techniques
Towards optimal circuit layout using advanced search techniques
Reporting of standard cell placement results
Proceedings of the 2001 international symposium on Physical design
FAR: fixed-points addition & relaxation based placement
Proceedings of the 2002 international symposium on Physical design
A force-directed macro-cell placer
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
Implementation and extensibility of an analytic placer
Proceedings of the 2004 international symposium on Physical design
Proceedings of the 2004 international symposium on Physical design
Supply Voltage Degradation Aware Analytical Placement
ICCD '05 Proceedings of the 2005 International Conference on Computer Design
Multilevel expansion-based VLSI placement with blockages
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Engineering details of a stable force-directed placer
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Integer Linear Programming Models for Global Routing
INFORMS Journal on Computing
Lens aberration aware placement for timing yield
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Stochastic power/ground supply voltage prediction and optimization via analytical placement
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Multithreaded memetic algorithm for VLSI placement problem
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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Traditionally, analytic placement used linear or quadratic wirelength objective functions. Minimizing either formulation attracts cells sharing common signals (nets) together. The result is a placement with a great deal of overlap among the cells. To reduce cell overlap, the methodology iterates between global optimization and repartitioning of the placement area. In this work, we added new attractive and repulsive forces to the traditional formulation so that overlap among cells is diminished without repartitioning the placement area. The superiority of our approach stems from the fact that our new formulations are convex and no hard constraints are required. A preliminary version of the new placement method is tested using a set of MCNC benchmarks1 and, on average, the new method achieved 3.96% and 7.6% reduction in wirelength and CPU time compared to TimberWolf v7.0 in hierarchical mode [10].