Net Clustering Based Constructive and Iterative Improvement Approaches for Macro-Cell Placement

  • Authors:
  • Stelian Alupoaei;Srinivas Katkoori

  • Affiliations:
  • Member, IEEE, Intel, OR, USA;Senior Member, IEEE, University of South Florida, Tampa, FL, USA

  • Venue:
  • Journal of VLSI Signal Processing Systems
  • Year:
  • 2004

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Abstract

We propose a net clustering based RT-level macro-cell placement approaches. Static timing analysis identifies critical nets and critical primary input/output paths. Net clustering (based on shared macro-cells and net criticality) yields clusters wherein each cluster has strongly interdependent nets. The circuit is modeled as a graph in which each vertex v represents a net and each edge (v,u) a shared cell between nets v and u. The net clusters are obtained by applying a clique partitioning algorithm on the circuit graph. Two approaches to generate placements at RTL are proposed: constructive (cluster growth) approach and iterative improvement (simulated annealing) based approach. In the constructive approach, a cluster-level floorplanning is performed and a cluster ordering is obtained. The cluster ordering is used by a constructive procedure to generate the physical placement. In the case of iterative improvement based approach, a good ordering of clusters is obtained using simulated annealing.We report experimental results for five RTL datapaths implemented in 0.35 μm technology to demonstrate the efficacy of the proposed approaches. We compared the layouts produced by our approaches with those produced by Flint, an automatic floor planner in Lager IV Silicon Compiler [1]. For constructive placement approach, we obtained an average decrease of 43.4% in longest wirelength and 32.4% in total wirelength. The average area reduction is 7.3%. On the other hand, for the SA-based approach, we obtained an average decrease of 57.6% in longest wirelength and 42.2% in total wirelength. The average reduction in the bounding-box area is 12.3%. As expected, the SA-based approach yielded better optimization results, due to its ability to climb out of local minima.