An efficient quadratic placement based on search space traversing technology

Authors:
Yongqiang Lu;Xianlong Hong;Qiang Zhou;Yici Cai;Jun Gu
Affiliations:
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China;Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China;Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China;Department of Computer Science and Technology, Tsinghua University, Beijing 100084, PR China;Department of Computer Science, Hong Kong University of Science and Technology, Hong Kong
Venue:
Integration, the VLSI Journal
Year:
2007

Citing 7
Cited 1

Search space reduction through clustering in test generation

EURO-DAC '95/EURO-VHDL '95 Proceedings of the conference on European design automation
VISI Physical Design Automation: Theory and Practice

VISI Physical Design Automation: Theory and Practice
Corner block list: an effective and efficient topological representation of non-slicing floorplan

Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
A Search Space Toolkit

CAIA '95 Proceedings of the 11th Conference on Artificial Intelligence for Applications
FaSa: a fast and stable quadratic placement algorithm

Journal of Computer Science and Technology
SSTT: efficient local search for GSI global routing

Journal of Computer Science and Technology
GORDIAN: VLSI placement by quadratic programming and slicing optimization

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Criticality history guided FPGA placement algorithm for timing optimization

Proceedings of the 18th ACM Great Lakes symposium on VLSI

Quantified Score

Hi-index	0.00

Visualization

Abstract

A very large-scale standard cell placement problem has too complicated solution space for conventional analytical quadratic placement methods to achieve the ''optimal'' or near-optimal solution in it. The rugged terrain of solution space makes those methods easy to get stuck at local optima. In this paper, a novel quadratic placement based on search space traversing technology is proposed to search the optimal or near-optimal solution. This method first employs a pre-partitioning to cut down the problem scale and reconstruct the problem structure, and then combines the Lagrange relaxation method and the Lagrange multipliers method together in quadratic placement to solve the global placement. Finally, after eliminating overlaps, a full-chip force-directed post-adjustment is employed to reduce the negative effect of pre-partitioning. Experimental results on benchmarks show encouraging results.