NDLI: Large scale circuit partitioning with loose/stable net removal and signal flow based clustering

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Large scale circuit partitioning with loose/stable net removal and signal flow based clustering

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Java as a specification language for hardware-software systems

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Test and diagnosis of fault logic blocks in FPGAs

Partial scan delay fault testing of asynchronous circuits

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Timing analysis and optimization (tutorial): from devices to systems

Critical technologies and methodologies for systems-on-chips (tutorial)

Modeling and synthesis of behavior, control and dataflow (tutorial)

Design technology for building wireless systems (tutorial)

Large scale circuit partitioning with loose/stable net removal and signal flow based clustering

Content Provider	ACM Digital Library
Author	Shibuya, Toshiyuki Li, Honching Peter Lim, Sung Kyu Xu, Dongmin Cong, Jason
Abstract	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-PROPf, 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, FBB, and PANZA by 32.0%, 21.4%, and 1.4%, respectively.
Starting Page	441
Ending Page	446
Page Count	6
File Format	PDF
ISBN	0818682000
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1997-11-13
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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