In this paper, we present an efficient Iterative Improvement based Partitioning (IIP) algorithm called LSR/MFFS, that combines signal flow based Maximum Fanout Free Subgraph (MFFS) clustering algorithm with Loose and Stable net Removal (LSR) partitioning algorithm. The MFFS algorithm generalizes existing MFFC decomposition method from combinational circuits to general sequential circuits in order to handle cycles naturally. We also study the properties of the nets that straddle the cutline carefully, and introduce the concepts of the loose and stable nets as well as effective ways to remove them out of the cutset. The LSR/MFFS algorithm first applies LSR algorithm to clustered netlist generated by MFFS algorithm for global-level cutsize optimization and then declusters netlist for further cutsize refinement. As a result, the LSR/MFFS algorithm has achieved the best cutsize result among all the bipartitioning algorithms published in the literatures with very promising runtime performance. In particular, it outperforms the recent state-of-the-art IIP algorithms LA3-CDIP, CLIP-PROP/sub f/, Strawman, hMetis-FM, and MLc by 17.4%, 12.1%, 5.9%, 3.1%, and 1.9%, respectively. It also outperforms the state-of-the-art non-IIP algorithms Paraboli, FEB, and PANZA by 32.0%, 21.4%, and 1.4%, respectively.
['Jason Cong', 'Honching Peter Li', 'Sung Kyu Lim', 'Toshiyuki Shibuya', 'Dong-Min Xu']
Large scale circuit partitioning with loose/stable net removal and signal flow based clustering