Presentation
Multi-Level Intelligent Waveform Orchestration: Specification-Aware Framework for Eliminating Storage-Debug Tradeoffs
DescriptionTraditional FSDB (Fast Signal Database) capture forces verification teams into an impossible choice: capture everything and face unsustainable storage costs, or capture selectively and risk missing critical debug data. We present a three-tier intelligent framework with live dashboard that eliminates this tradeoff, achieving >90% storage reduction while improving debug coverage through specification-aware adaptive intelligence.
Tier 1: Global Policy Intelligence - A centralized database enables architects to define capture strategies for thousands of tests through pattern-based rules, achieving 10× policy reuse vs. per-test hardcoding. This layer ensures critical baseline tests always capture while intelligently filtering stable tests.
Tier 2: Specification-Aware Runtime Adaptation - Compares actual performance against expected specifications in real-time. Bandwidth degradation, credit starvation, and protocol violations trigger targeted captures only when sustained deviations occur, reducing false triggers by >75% compared to traditional approaches.
Tier 3: Regression-Wide Cross-Test Learning - Enables fast tests to inform still-running tests about systemic issues, with hints containing precise timing windows for surgical FSDB chunks rather than full dumps. Engineers can manually override policies during live regressions or use automated steering for pending tests.
Demonstrated results include 92.5% storage reduction, 50× faster time-to-root-cause through cross-test coordination, and 10× policy reuse—all achieved with zero simulation overhead, enabling verification teams to break free from the traditional storage-debug tradeoff.
Tier 1: Global Policy Intelligence - A centralized database enables architects to define capture strategies for thousands of tests through pattern-based rules, achieving 10× policy reuse vs. per-test hardcoding. This layer ensures critical baseline tests always capture while intelligently filtering stable tests.
Tier 2: Specification-Aware Runtime Adaptation - Compares actual performance against expected specifications in real-time. Bandwidth degradation, credit starvation, and protocol violations trigger targeted captures only when sustained deviations occur, reducing false triggers by >75% compared to traditional approaches.
Tier 3: Regression-Wide Cross-Test Learning - Enables fast tests to inform still-running tests about systemic issues, with hints containing precise timing windows for surgical FSDB chunks rather than full dumps. Engineers can manually override policies during live regressions or use automated steering for pending tests.
Demonstrated results include 92.5% storage reduction, 50× faster time-to-root-cause through cross-test coordination, and 10× policy reuse—all achieved with zero simulation overhead, enabling verification teams to break free from the traditional storage-debug tradeoff.
Event Type
Engineering Poster
TimeTuesday, July 285:00pm - 6:00pm PDT
LocationDAC Pavilion, Exhibit Floor
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