Beyond the Structural Filter: AI-Assisted Inductive Refinement for Scalable Formal Sign-Off of Multi-Stage Arbitration Logic
Abstract
Achieving formal convergence for multi-stage arbitration logic in high-concurrency controllers, such as 4x4 interface crossbar nodes, is frequently impeded by high Cone of Influence (COI) and complex property interdependencies. While helper assertions are critical for inductive proofs, the strategic selection and grouping of these assertions for Compositional Assume-Guarantee (CAG) reasoning remains a significant manual bottleneck .
This paper proposes a tiered, data-driven methodology that shifts the verification paradigm from deep sequential searching to single-cycle inductive transitions. Our approach follows an iterative, three-tiered workflow:
Tier 1 (Structural): Utilizes structural COI analysis for initial grouping.
Tier 2 (Computational): Employs Formal Core analysis to prune the selection to the logic subset actually exercised by the engine .
Tier 3 (AI-Assisted): Leverages a Large Language Model (LLM) to perform 'Impact Grading' on candidate assertions, isolating the 'Golden Triangle' of high-impact inductive invariants (State Mapping, Ingress Consistency, and Control-Path Sync).
By utilizing the CAG inductive mechanism, this methodology successfully resolved deep sequential stalls in critical Forward Progress and Data Integrity checks. Results demonstrate that this tiered refinement reduces multi-day verification efforts to just a few hours, achieving full verification completeness and establishing a repeatable framework for formal sign-off on complex architectural features.
This paper proposes a tiered, data-driven methodology that shifts the verification paradigm from deep sequential searching to single-cycle inductive transitions. Our approach follows an iterative, three-tiered workflow:
Tier 1 (Structural): Utilizes structural COI analysis for initial grouping.
Tier 2 (Computational): Employs Formal Core analysis to prune the selection to the logic subset actually exercised by the engine .
Tier 3 (AI-Assisted): Leverages a Large Language Model (LLM) to perform 'Impact Grading' on candidate assertions, isolating the 'Golden Triangle' of high-impact inductive invariants (State Mapping, Ingress Consistency, and Control-Path Sync).
By utilizing the CAG inductive mechanism, this methodology successfully resolved deep sequential stalls in critical Forward Progress and Data Integrity checks. Results demonstrate that this tiered refinement reduces multi-day verification efforts to just a few hours, achieving full verification completeness and establishing a repeatable framework for formal sign-off on complex architectural features.
