Machine learning: paradigms and methods
Machine learning: paradigms and methods
System Design with SystemC
IODINE: a tool to automatically infer dynamic invariants for hardware designs
Proceedings of the 42nd annual Design Automation Conference
SystemC: From the Ground Up
Interactive presentation: PowerQuest: trace driven data mining for power optimization
Proceedings of the conference on Design, automation and test in Europe
Verification methodologies in a TLM-to-RTL design flow
Proceedings of the 44th annual Design Automation Conference
Markov Chains and Stochastic Stability
Markov Chains and Stochastic Stability
Proceedings of the 48th Design Automation Conference
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Adopting an ESL based design and validation methodology, we introduce a top-down approach for efficient debugging of microarchitectural specification and RTL implementation. Our solution is based on the formalism introduced by statistical transactional analysis that we call MAGENTA -- Modeling AGENT for Transactional Analysis. To the best of our knowledge, MAGENTA based root-cause analysis pioneers in the efficient characterization of the micro-architectural design misbehavior via abstraction of validation output by transactions and micro-architectural events.