Limitations and Opportunities for Wire Length Prediction in Gigascale Integration

by Anbalagan, Pranav

Abstract (Summary)
Wires have become a major source of bottleneck in current VLSI designs, and wire length prediction is therefore essential to overcome these bottlenecks. Wire length prediction is broadly classified into two types: macroscopic prediction, which is the prediction of wire length distribution, and microscopic prediction, which is the prediction of individual wire lengths. The objective of this thesis is to develop a clear understanding of limitations to both macroscopic and microscopic a priori, post-placement, pre-routing wire length predictions, and thereby develop better wire length prediction models. Investigations carried out to understand the limitations to macroscopic prediction reveal that, in a given design (i) the variability of the wire length distribution increases with length and (ii) the use of Rents rule with a constant Rents exponent p, to calculate the terminal count of a given block size, limits the accuracy of the results from a macroscopic model. Therefore, a new model for the parameter p is developed to more accurately reflect the terminal count of a given block size in placement, and using this, a new more accurate macroscopic model is developed. In addition, a model to predict the variability is also incorporated into the macroscopic model. Studies to understand limitations to microscopic prediction reveal that (i) only a fraction of the wires in a given design are predictable, and these are mostly from shorter nets with smaller degrees and (ii) the current microscopic prediction models are built based on the assumption that a single metric could be used to accurately predict the individual length of all the wires in a design. In this thesis, an alternative microscopic model is developed for the predicting the shorter wires based on a hypothesis that there are multiple metrics that influence the length of the wires. Three different metrics are developed and fitted into a heuristic classification tree framework to provide a unified and more accurate microscopic model.
Bibliographical Information:

Advisor:Dr. Sung Kyu Lim; Dr. Scott Wills; Dr. James D. Meindl; Dr. Jeff Davis; Dr. Paul Kohl

School:Georgia Institute of Technology

School Location:USA - Georgia

Source Type:Master's Thesis

Keywords:electrical and computer engineering


Date of Publication:02/21/2007

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