1 / 25

A Stable Fixed-outline Floorplanning Method

Song Chen and Takeshi Yoshimura Graduate School of IPS, Waseda University March, 2007. A Stable Fixed-outline Floorplanning Method. Outline. Problem Previous Work Fixed-outline Floorplanning Overview Objective Function Solution Perturbation Experimental Results Conclusions. H 0. W 0.

nau
Download Presentation

A Stable Fixed-outline Floorplanning Method

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Song Chen and Takeshi Yoshimura Graduate School of IPS, Waseda University March, 2007 A Stable Fixed-outline Floorplanning Method

  2. Outline • Problem • Previous Work • Fixed-outline Floorplanning • Overview • Objective Function • Solution Perturbation • Experimental Results • Conclusions

  3. H0 W0 Problem • Given • A set of rectangular blocks among which connections (nets) exist • Specified width wi and height hi for each block bi • Specified rectangular region: W0, H0. (Fixed-outline) • The fixed-outline floorplanning is to determine coordinates for each block such that • There is no overlapping between any two blocks. • All the blocks are placed inside the specified region (fixed-outline) • Some objectives, such as wire-length, etc., are optimal.

  4. Outline • Problem • Previous Work • Fixed-outline Floorplanning • Overview • Objective Function • Solution Perturbation • Experimental Results • Conclusions

  5. Previous Work • S. Adya and I. Markov, ICCD’01 TCAD’03 (Parquet) • New objective functions; New types of move. • C. Lin, et al., ASPDAC’04 • Evolutionary search-based robust fixed-outline floorplanning; Fixed-outline constraint only. • R. Liu et al., ISCAS’05. • Instance augmentation; Fixed-outline constraint only. • T.C. Chen and Y.W. Chang, ISPD’05. • Adaptive Fast-SA; Weights in the cost function changed Dynamically.

  6. Previous Work (Cont’) • The existing fixed-floorplanning methods work well when fixed-outline constraint is the only objective. • Poor success rates when optimizing wire and other objectives. • And when the aspect ratios are far away from one (W=H).

  7. Outline • Problem • Previous Work • Fixed-outline Floorplanning • Overview • Objective Function • Solution Perturbation • Experimental Results • Conclusions

  8. Overview of Floorplanning • Sequence Pair is used for floorplan representation • Objective function • Solution perturbation • Remove a block randomly • Compute the floorplan of the blocks except the removed one • Select fixed number of candidate insertion points for the removed block by enumerating insertion points • Choose for the removed block one of the candidate insertion points randomly

  9. Outline • Problem • Previous Work • Fixed-outline Floorplanning • Overview • Objective Function • Solution Perturbation • Experimental Results • Conclusions

  10. Ew Eh H0 Fixed-outline W0 Objective Function • Objective functions used in the existing fixed-outline floorplanners. • Low success rate when given larger aspect ratios. • Low success rate when other objectives exist. • since the function values hardly reach zero when competitions from other objectives exist. • A trade-off between area and aspect ratios.

  11. Objective Functions (Cont’) • Calculate chip area costs for fixed-outline floorplanning (assume λ>1) • EW = max(W −W0, 0) • EH = max(H − H0, 0) • C1 and C2 are user-defined constants • λ is the aspect ratio. • High success rates for large aspect ratios • High success rate when combined with other objectives Ew EH H0 W0

  12. Outline • Problem • Previous Work • Fixed-outline Floorplanning • Overview • Objective Function • Solution Perturbation • Experimental Results • Conclusions

  13. Solution Perturbation –Enhanced Remove and Insertion • Remove a block randomly • Insert the block • Select some candidate insertion points (CIP, totally 100 here) by Enumerating InsertionPoints (EIP) (rough estimation) • Select from the CIPs the insertion point for the removed block

  14. Enumerate Insertion Points (EIP) • Sequence Pair (P, M) • (…bi…bj…, …bi…bj…)  bj is left to bi • (…bi…bj…, …bj…bi…)  bj is below bi • An insertion point means one position in P and one position M -- (p, m) • In order to evaluate an insertion point, we need to know how much inserting a block into the insertion point will contribute to the chip width and height

  15. EIP – Computing x-coordinates • Given a Sequence Pair (P, M) • Coordinates (with origin at the bottom-left corner of the chip) of a block bi only depend on the blocks that are left to biin the sequence M • Coordinates of the blocks that are right to bi in both P and M are larger than that of bi ( a b c e d f g, a c b d e g f ) ( a b c e d f g, a c b d e g f )

  16. EIP— Computing x-coordinates (Cont’) • Based on the previous observations, we can compute the x-coordinates of all insertion points • Given a sequence pair (P, M) = (f c e d b a, c b f a d e) Distance of CIPs (p, b+) to the left boundary: p is before c in P, 0; p is between b and c in P, 2; p is after b: 4. Distance of CIPs (p, c+) to the left boundary: p is before c in P, 0; p is after c in P: 2. ( f c e d b a, c b f a d e ) ( f c e d b a, c b f a d e )

  17. Enumerating Insertion Points • Following pairs of sequences are scanned to compute the distance of an insertion point to the chip boundaries • (P, M): Distance to the left boundary • (Pr, M): Distance to the bottom boundary • (Pr, Mr): Distance to the right boundary • (P, Mr): Distance to the top boundary top P Mr left right M Pr bottom

  18. Enumerating Insertion Points (Cont’) • The enumerating is similar to the computation of x-coordinates, but, for each time, we have to scan four lists simultaneously. • Without consideration of wire length, the complexity of enumerating is O(n2), which is linear with the number of insertion points. • During the enumerating, we take into account only the nets that have connections to the removed block. • a linear piecewise function is used for wire-length calculation.

  19. Outline • Problem • Previous Work • Fixed-outline Floorplanning • Overview • Objective Function • Solution Perturbation • Experimental Results • Conclusions

  20. Experimental Results-Success Rate • white space percent 10%, all blocks are hard, and the aspect ratios are chosen from the range [1,3] with interval 0.5. • Success rate: Parquet (SP) 60%, Parquet (BTree) 100%, NTU-FOFP 94%, IARFP 100%. • Runtime: IARFP is the least one. (a tenth part)

  21. Experimental results-Wire • White space 10%, 50 runs for n100, 10 runs for n200 and n300. • Success rate: IARFP 100%, NTU-FOFP 45%, andParquet(SP) 34% • Wire: IARFPachieved 12% and 7% improvement • Runtime: IARFP spent much less time.

  22. Experimental Results-Objective Function • Embed objective function into the existing fixed-outline floorplanner NTU-FP • White space: 10% • Aspect ratios: From the range [1,3] with interval 0.5

  23. Outline • Problem • Previous Work • Fixed-outline Floorplanning • Overview • Objective Function • Solution Perturbation • Experimental Results • Conclusions

  24. Conclusions • We developed a stable fixed-outline floorplanner • A new method for calculating area costs in fixed-outline floorplanning is proposed. • An enhanced remove and insertion solution perturbation method is implemented based on enumerating insertion points. • Compared with the existing method, the proposed method is very effective and efficient.

  25. Thanks for your attentions!

More Related