Mastering Advanced Debug in Conformal LEC: Mapping to AI Driven Abort Resolution

From manual debugging to AI-assisted resolution-LEC (logical equivalence checking) debug is evolving.

Join us to explore how traditional Conformal Equivalence Checker (LEC) and next-generation Conformal AI Equivalence technologies can transform the way you handle non-equivalences (NEQs) and aborts.

CadenceTECHTALK: Conformal Equivalence Checker Advanced Debug

What This Training Covers

The presentation is structured around a clear, methodical debug flow that mirrors real‑world LEC challenges encountered during RTL‑to‑netlist and hierarchical comparisons.

A. Mapping: The Foundation of Successful LEC

The training emphasizes mapping as the first checkpoint before diving into deeper debug. Participants are introduced to:

• Mapping resolution charts
• Common causes of unmapped points
• Best practices for resolving naming, modeling, and data consistency issues

Correct mapping ensures that corresponding key points between golden and revised designs are paired accurately, forming the basis for meaningful equivalence analysis. While automated out-of-box mapping has improved dramatically in the new Conformal AI Studio product line, this continues to be a critical step in the flow to avoid false NEQs and aborts.

B. Debugging Non‑Equivalences with Structured Resolution Charts

Once mapping is validated, the focus shifts to non‑equivalent compare points. The training categorizes non‑equivalences into:

• False non‑equivalences caused by setup, constraint, modeling, or mapping issues
• True non‑equivalences resulting from real functional differences

Attendees learn how to use non‑equivalence resolution charts to triage failures, investigate schematics, validate constraints, and identify when issues originate from synthesis optimizations or RTL behavior.

C. Abort Analysis and Resolution

Aborts are highlighted as a distinct class of LEC challenges—compare points that cannot be proven equivalent or non equivalent within allocated runtime. The session explains:

• Typical causes of aborts, such as large combinational cones, resource sharing, and X‑assignments
• Why aborts should be debugged only after resolving non‑equivalences
• How abort resolution charts guide engineers toward effective next steps

This structured approach helps reduce guesswork and accelerates convergence. Conformal approaches to analyzing and solving aborts have been evolving over the last decade. Multiple technologies are available in traditional LEC, while some of the newest automated approaches require Conformal AI Equivalence.

D. Leveraging Conformal LEC and Smart LEC

To address runtime and scalability challenges, the training introduces advanced features including:

• Conformal LEC techniques for improving performance
• Conformal Smart LEC capabilities such as multithreading, smart instance selection, and distributed parallel analysis

These features are positioned as key enablers for faster turnaround time, especially on large, hierarchical designs.

E. AI‑Driven Automated Abort Resolution with CAR

The session culminates with a look at Conformal AI Equivalence and the CAR (Conformal Abort Resolution) flow. By leveraging historical run data, machine learning models, and automated recipe exploration, CAR significantly reduces the time required to resolve stubborn aborts. CAR uses the Cerebrus reinforcement learning architecture to explore and manage multiple Conformal scenarios in parallel to find the optimal resolving recipes for any aborting modules.

This represents a shift from manual trial‑and‑error toward data‑driven, intelligent debug, especially valuable for complex designs with recurring abort patterns.

Conclusion

The Conformal Equivalence Checker Advanced Debug training equips engineers with a clear, repeatable methodology for tackling some of the toughest LEC challenges. By combining proven resolution charts, performance‑enhancing features like Smart LEC, and next‑generation AI‑driven flows such as CAR, the session demonstrates how advanced debug can move from reactive troubleshooting to proactive optimization.

For engineers who already understand the fundamentals of LEC and want to debug smarter, converge faster, and scale with confidence, this training provides both the mindset and the tools to do exactly that.