A Layered Governance Framework for AI-Enabled Pre-Silicon Validation Infrastructure

Abstract

Validation organizations have already employed machine learning one decision at a time‚ whether a scheduling model here‚ a triage classifier there‚ or a compile-time predictor grafted onto an existing regression flow․ Each new model brought a task-level gain․ Such questions of composition‚ how an authorized party acts on the output of a model‚ and how a party comes to trust the output before taking a schedule-critical action are often unanswered․ This article proposes that validation-infrastructure AI belongs in a single‚ multi-layered‚ platform architecture with an explicit orchestration and governance layer‚ between the model output and the action that gets executed‚ rather than as separate services adjacent to each of the many tools․ Meanwhile‚ validation-pipeline choices are coupled in terms that a point solution cannot see: a partitioning choice shifts build time‚ which shifts debug fidelity‚ which shifts how much of a verification state space a program can credibly claim to have covered by tape-out․ Drawing from MLOps literature‚ explainable-AI literature‚ and task-specialized EDA/ML literature‚ a six-layer reference architecture is composed for the validation pipeline: data plane‚ data-plane ingestion‚ feature and knowledge management‚ model service‚ orchestration and decision‚ engineer interface‚ and cross-cutting governance․ Architectural analysis of the evaluation shows that guarantees of trustworthiness are required (more than in normal enterprise machine learning deployments) for irreversible‚ time-critical‚ tape-out-adjacent validation․ These guarantees are motivated by the author's experience leading emulation infrastructure investment across four generations of flagship programs․ The claim is almost a narrow one: the next real gain in validation productivity looks architectural‚ not like one more point-solution model․