Solutions to the module orientation and rotation problems by neural computation networks
DAC '89 Proceedings of the 26th ACM/IEEE Design Automation Conference
The Orientation of Modules Based on Graph Decomposition
IEEE Transactions on Computers
Consistent placement of macro-blocks using floorplanning and standard-cell placement
Proceedings of the 2002 international symposium on Physical design
Flipping Modules to Minimize Maximum Wire Length
ICCD '91 Proceedings of the 1991 IEEE International Conference on Computer Design on VLSI in Computer & Processors
The ISPD2005 placement contest and benchmark suite
Proceedings of the 2005 international symposium on Physical design
Wirelength optimization by optimal block orientation
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Pattern sensitive placement for manufacturability
Proceedings of the 2007 international symposium on Physical design
Block flipping and white space distribution for wirelength minimization
Integration, the VLSI Journal
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In a placed circuit, there are a lot of movable cells that can be flipped to further reduce the total wirelength, without affecting the original placement solution. We aim at solving this flipping problem optimally. However, solving such a problem optimally is non-trivial given the gigantic sizes of modern circuits. We are able to identify a large portion of cells (about 75%) of which the orientation (flipped or not flipped) can be determined independent of the orientations of all the other cells. We have derived three non-trivial conditions to identify those so called independent cells, strictly solvable cells and conditionally solvable cells. In this way, we can greatly reduce the number of cells whose orientations are dependent on each other. Finally, the cell flipping problem of the remaining dependent cells can be formulated as a Mixed Integer Linear Programming (MILP) problem and solved optimally. However, this may still be too slow for extremely large circuits and we have applied two other methods, Linear Programming (LP) and Linear Programming followed by Mixed Integer Linear Programming (LP+MILP) to solve the problem. Experimental results show that by identifying those independent and solvable cells first and applying the LP+MILP technique, we can solve this flipping problem effectively and obtain results just 0.01% more than the optimal. In addition, we can improve the wirelength and number of overflow tiles by 5% and 9% respectively on the floorplanning benchmarks.