Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
On Silicon-Based Speed Path Identification
VTS '05 Proceedings of the 23rd IEEE Symposium on VLSI Test
Silicon speedpath measurement and feedback into EDA flows
Proceedings of the 44th annual Design Automation Conference
Speedpath prediction based on learning from a small set of examples
Proceedings of the 45th annual Design Automation Conference
Statistical diagnosis of unmodeled systematic timing effects
Proceedings of the 45th annual Design Automation Conference
Proceedings of the 47th Design Automation Conference
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
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In optimizing high-performance designs, speed limiting paths (speed-paths) impact the performance and power trade-off. Timing tools attempt to model and capture all such paths on a chip. Due to the high performance nature of these designs, critical paths predicted by the timing tools often do not match the actual speedpaths found on silicon chips. Early silicon data therefore is used to identify the speedpaths, and further performance optimization is carried out by pushing the delays on these paths. In this context, the paper presents a novel data mining approach that analyzes a small number of identified speedpaths against a large number of non-speedpaths. The result of this analysis for each speedpath is a set of hypotheses explaining why the path is special. These hypotheses can be used in guiding the search for the root causes, or in predicting additional paths as potential speedpaths. We demonstrate the feasibility of this approach and summarize our findings based on analysis of silicon speedpaths collected from a 65nm microprocessor.