Analytical placement: A linear or a quadratic objective function?
DAC '91 Proceedings of the 28th ACM/IEEE Design Automation Conference
NRG: global and detailed placement
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
ALPS2: a standard cell layout system for double-layer metal technology
DAC '85 Proceedings of the 22nd ACM/IEEE Design Automation Conference
PLINT layout system for VLSI chips
DAC '85 Proceedings of the 22nd ACM/IEEE Design Automation Conference
A class of min-cut placement algorithms
DAC '77 Proceedings of the 14th Design Automation Conference
On mismatches between incremental optimizers and instance perturbations in physical design tools
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
Quality of EDA CAD Tools: Definitions, Metrics and Directions
ISQED '00 Proceedings of the 1st International Symposium on Quality of Electronic Design
Placement Method Targeting Predictability Robustness and Performance
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
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T raditional placement problems are studied under a fully specified cell library and a complete netlist. Ho w ev er, in the first, e.g., 2 years of a 2-3 year microprocessor design cycle, the detailed netlist is una vailable. F or area and performance estimation, layout must nev ertheless be done with incomplete information. Another source of incompleteness comes from reuse of instances from earlier design generations; these instances and their parameters will c hange as the project evolves. The problem of placement with incomplete data (PID) can be abstracted as ha ving to place a circuit when pn% of the nets are missing. The key challenge in PID is how to add missing cells and nets.In this paper, tw o “patc hing-methods” for adding missing nets and cells are proposed. The methods are called abstraction and fusion.Experimental results are v ery in teresting and illurstrative. First, they sho w that PID is a difficult problem and an arbitrary (and perhaps intuitiv ely sound) method may not produce high-quality results. Experiments verify that the abstraction method is a very good predictor and that fusion is not because circuits produced by abstraction attain much of the properties of the original circuits. Summary Table 3 in Section 4 shows that when a circuit has 10% incompleteness, abstraction can predict the final total wirelength with an error of 5.8%, while fusion has a 67.8% error in predicting the wirelength in the same circuit.