Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Transaction level modeling: an overview
Proceedings of the 1st IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Hybrid, Incremental Assertion-Based Verification for TLM Design Flows
IEEE Design & Test
A Tractable and Fast Method for Monitoring SystemC TLM Specifications
IEEE Transactions on Computers
Scalable specification mining for verification and diagnosis
Proceedings of the 47th Design Automation Conference
GoldMine: automatic assertion generation using data mining and static analysis
Proceedings of the Conference on Design, Automation and Test in Europe
Automatic assertion extraction via sequential data mining of simulation traces
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Logic of constraints: a quantitative performance and functional constraint formalism
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Word level feature discovery to enhance quality of assertion mining
Proceedings of the International Conference on Computer-Aided Design
Data mining MPSoC simulation traces to identify concurrent memory access patterns
Proceedings of the Conference on Design, Automation and Test in Europe
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Diagnosing performance violations is one of the biggest challenges in transaction level modeling of systems. In this paper, we propose a methodology to localize root causes of latency or throughput violations. We present a concurrent pattern mining approach to infer frequent patterns from transaction traces to localize root causes. We apply three categories of domain knowledge from the violation and models to filter the irrelevant transaction traces and increase the effectiveness of the mining results. We provide three culprit scenarios to mining algorithm by including transaction traces relevant to the corresponding culprit scenario. The mined concurrent patterns then belong to that culprit scenario. We provide a case study for diagnosing performance violations of an experimental platform and show that our domain knowledge can reduce the number of transaction traces by up to 92.8%. The concurrent pattern mining pinpoints the root cause to one of fewer than 10 patterns among 100000 transaction traces.