Presentation
Auto-Chk: Closing the Last-Mile Interpretation Gap in Digital Sign-off via a Neuro-Symbolic Compiler Framework
DescriptionRecent RCA studies show that failures in digital sign-off rarely stem from EDA execution itself, but from the last-mile interpretation layer. Script-based automation, built on rigid syntax matching, breaks under tool/version drift and cannot enforce cross-stage causality, leading to silent misses. Human-centric checklists, introduced as a safety net, collapse under massive unstructured reports and schedule pressure, causing cognitive overload and false sign-off escapes.
We present Auto-Chk, a neuro-symbolic compiler framework that closes this interpretation gap. Instead of hard-coding scripts, Auto-Chk compiles natural-language verification intent into a deterministic intermediate representation, ItemSpec, which decouples intent from implementation. Generated checkers are stress-tested using adversarial, metamorphic validation without requiring golden data, and executed inside a secure sandbox with runtime monitoring. A cross-layer self-healing loop automatically adapts to log or tool changes. On top, an evidence-driven dashboard and role-based copilots transform raw logs into confidence-aware decisions and organizational knowledge.
Auto-Chk has been deployed at production scale, covering over 120 checklist items across multiple IPs, EDA tool versions, and process nodes. Results demonstrate a 20× reduction in checker development time, 100% semantic consistency, and sub-30-minute time to recovery, with full project convergence achieved within 15 hours. Outputs are delivered through a project-level dashboard and a role-based copilot, providing executable, resilient sign-off intelligence.
We present Auto-Chk, a neuro-symbolic compiler framework that closes this interpretation gap. Instead of hard-coding scripts, Auto-Chk compiles natural-language verification intent into a deterministic intermediate representation, ItemSpec, which decouples intent from implementation. Generated checkers are stress-tested using adversarial, metamorphic validation without requiring golden data, and executed inside a secure sandbox with runtime monitoring. A cross-layer self-healing loop automatically adapts to log or tool changes. On top, an evidence-driven dashboard and role-based copilots transform raw logs into confidence-aware decisions and organizational knowledge.
Auto-Chk has been deployed at production scale, covering over 120 checklist items across multiple IPs, EDA tool versions, and process nodes. Results demonstrate a 20× reduction in checker development time, 100% semantic consistency, and sub-30-minute time to recovery, with full project convergence achieved within 15 hours. Outputs are delivered through a project-level dashboard and a role-based copilot, providing executable, resilient sign-off intelligence.
Event Type
Engineering Poster
TimeTuesday, July 285:00pm - 6:00pm PDT
LocationDAC Pavilion, Exhibit Floor
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