Analytical placement: A linear or a quadratic objective function?
DAC '91 Proceedings of the 28th ACM/IEEE Design Automation Conference
Algorithms for large-scale flat placement
DAC '97 Proceedings of the 34th annual Design Automation Conference
Faster minimization of linear wirelength for global placement
Proceedings of the 1997 international symposium on Physical design
Can recursive bisection alone produce routable placements?
Proceedings of the 37th Annual Design Automation Conference
PROUD: A Sea-Of-Gates Placement Algorithm
IEEE Design & Test
Proceedings of the 2004 international symposium on Physical design
Large-scale placement by grid-warping
Proceedings of the 41st annual Design Automation Conference
The ISPD2005 placement contest and benchmark suite
Proceedings of the 2005 international symposium on Physical design
Diffusion-based placement migration
Proceedings of the 42nd annual Design Automation Conference
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
Architecture and details of a high quality, large-scale analytical placer
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Legalizing a placement with minimum total movement
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Traditionally, research in global placement has focused on relatively few simple metrics, such as pure wirelength or routability estimates. However, in the real world today, designs are driven by not-so-simple issues such as timing and crosstalk. The future holds even more difficulties as physical models for devices and interconnects become increasingly complex and unpredictable. Adoption of an iterative methodology, where one incrementally fixes design errors, is a basic approach to tackling these problems. However, developers of placement algorithms have long neglected the need for an tool which can be easily adopted into an incremental design flow. We propose a novel placement approach called grid morphing, which is specifically tailored for an incremental approach to placement. In particular, our technique focuses on the stability of the placement, which is critical for minimization of perturbation of the final placement under changes applied to the input netlist. We comparethe stability of our approach to existing placement tools, and show through experiments that our approach still delivers good results under traditional placement metrics.