The nuts and bolts of physical synthesis
Proceedings of the 2007 international workshop on System level interconnect prediction
An effective clustering algorithm for mixed-size placement
Proceedings of the 2007 international symposium on Physical design
Fast and robust quadratic placement combined with an exact linear net model
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Yield-aware placement optimization
Proceedings of the conference on Design, automation and test in Europe
RQL: global placement via relaxed quadratic spreading and linearization
Proceedings of the 44th annual Design Automation Conference
Exploiting Spatial Locality for Objects Layout in Virtual Environments
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
WSEAS Transactions on Information Science and Applications
Handling complexities in modern large-scale mixed-size placement
Proceedings of the 46th Annual Design Automation Conference
SafeChoice: a novel clustering algorithm for wirelength-driven placement
Proceedings of the 19th international symposium on Physical design
SimPL: an effective placement algorithm
Proceedings of the International Conference on Computer-Aided Design
Sub-quadratic objectives in quadratic placement
Proceedings of the Conference on Design, Automation and Test in Europe
| Hi-index | 0.03 |
Placement is a critical component of today's physical-synthesis flow with tremendous impact on the final performance of very large scale integration (VLSI) designs. Unfortunately, it accounts for a significant portion of the overall physical-synthesis runtime. With the complexity and the netlist size of today's VLSI design growing rapidly, clustering for placement can provide an attractive solution to manage affordable placement runtimes. However, such clustering has to be carefully devised to avoid any adverse impact on the final placement solution quality. This paper presents how to apply clustering and unclustering strategies to an analytic top-down placer to achieve large speedups without sacrificing (and sometimes even enhancing) the solution quality. The authors' new bottom-up clustering technique, called the best choice (BC), operates directly on a circuit hypergraph and repeatedly clusters the globally best pair of objects. Clustering score manipulation using a priority-queue (PQ) data structure enables identification of the best pair of objects whenever clustering is performed. To improve the runtime of PQ-based BC clustering, the authors proposed a lazy-update technique for faster updates of the clustering score with almost no loss of the solution quality. A number of effective methods for clustering score calculation, balancing cluster sizes, handling of fixed blocks, and area-based unclustering strategy are discussed. The effectiveness of the resulting hierarchical analytic placement algorithm is tested on several large-scale industrial benchmarks with mixed-size fixed blocks. Experimental results are promising. Compared to the flat analytic placement runs, the hierarchical mode is 2.1 times faster, on the average, with a 1.4% wire-length improvement.