Empirical models for net-length probability distribution and applications
Davoodi, A.
Khandelwal, V.
Srivastava, A.
Coll. Park, Univ. of Maryland, College Park, MD, USA;
This paper appears in: Very Large Scale Integration (VLSI) Systems, IEEE Transactions on
Publication Date: Oct. 2004
Volume: 12,
Issue: 10
On page(s): 1066- 1075
ISSN: 1063-8210
INSPEC Accession Number: 8125408
Digital Object Identifier: 10.1109/TVLSI.2004.834235
Current Version Published: 2004-09-27
Abstract
In this paper, we propose a novel, empirical, and parameterizable model for estimating the probability distribution of wire length for each net in a placed netlist. The model is simple and fast to compute. We did extensive experimentation with state-of-the-art commercial (Cadence) and academic (Parquet and Labyrinth) tools and validated our model. Our distribution model was around three times more accurate than assuming half-perimeter bounding box as the fixed net-length estimate. Since the model is parameterizable it can be easily tailored for different routing tools and benchmarks. This model would be very useful in defining a full fledged probabilistic design automation methodology in which various design metrics are optimized from a probabilistic point of view. We also discuss the application of our model in a novel probabilistic approach to the buffer insertion problem.
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