Breaking the O(N^2) Barrier: AI-Assisted Linear-Time Formal Verification of Deep Pipeline Buffers
Abstract
Formal verification of deep tracking buffers (64+ stages) in AI and networking SoCs frequently hits a "Complexity Wall," where traditional Bounded Model Checking (BMC) scales quadratically (O(N2xW)) relative to pipeline depth. As sequential unrolling depth increases, the resulting state space explosion leads to inconclusive timeouts that prevent full architectural sign-off. This paper introduces an AI-assisted framework that shatters this barrier by shifting from deep sequential searching to a 1-cycle inductive proof, effectively achieving linear scalability (O(N xW)).
The core innovation is an LLM-driven "Impact Grading" framework that automates the arduous discovery of high-impact inductive invariants. By categorizing candidate properties into a tiered hierarchy (S/A/B), we identify the "Golden Triangle" of invariants—State Mapping, Ingress Consistency, and Control-Path Sync—that bridge the reference model and RTL state. This targeted approach eliminates the traditional manual bottleneck of invariant generation, which historically required weeks of expert engineering effort.
Experimental results on a 64-stage tracking buffer demonstrate a paradigm shift: while standard BMC remains inconclusive after 24 hours of computation, our methodology achieves 100% mathematical convergence in approximately 50 minutes. This framework enables comprehensive formal sign-off for high-latency, high-concurrency designs previously deemed "unverifiable," significantly accelerating the development cycle for next-generation hardware architectures.
The core innovation is an LLM-driven "Impact Grading" framework that automates the arduous discovery of high-impact inductive invariants. By categorizing candidate properties into a tiered hierarchy (S/A/B), we identify the "Golden Triangle" of invariants—State Mapping, Ingress Consistency, and Control-Path Sync—that bridge the reference model and RTL state. This targeted approach eliminates the traditional manual bottleneck of invariant generation, which historically required weeks of expert engineering effort.
Experimental results on a 64-stage tracking buffer demonstrate a paradigm shift: while standard BMC remains inconclusive after 24 hours of computation, our methodology achieves 100% mathematical convergence in approximately 50 minutes. This framework enables comprehensive formal sign-off for high-latency, high-concurrency designs previously deemed "unverifiable," significantly accelerating the development cycle for next-generation hardware architectures.
