AI-Enabled Validation Infrastructure: A Framework for Intelligent Semiconductor Platforms
About this session
Modern semiconductor platforms integrate tens of billions of transistors across heterogeneous compute engines, making pre-silicon validation one of the most resource-intensive stages of the development lifecycle. While artificial intelligence has advanced neighboring domains like physical design and formal verification, the infrastructure layer supporting validation, including build systems, emulator farms, schedulers, and telemetry repositories, has remained largely untouched by intelligent automation despite generating vast amounts of structured operational data every week. This talk presents a layered architectural framework for transforming validation infrastructure into an intelligent, adaptive platform. The framework spans data ingestion, feature and knowledge representation, model services, orchestration, and engineer-facing interfaces, unified under continuous governance and feedback loops. Key integration points include build optimization, partitioning, resource scheduling, timing prediction, debug instrumentation, and failure triage, each addressing decisions historically driven by static heuristics or manual judgment. Attendees will gain a structured understanding of where machine learning can meaningfully improve validation productivity and scalability, how to architect trustworthy AI systems that preserve engineer oversight and validation quality, and a maturity roadmap for progressing from instrumented baselines toward autonomous validation. The session is designed for semiconductor engineering leaders, validation architects, and AI practitioners exploring infrastructure-level transformation in silicon development.
Speaker
Key takeaways
- Validation infrastructure (builds, emulator farms, schedulers, telemetry) is a neglected but data-rich area ripe for AI — unlike physical design or formal verification, which already use ML.
- A layered architecture — data ingestion → feature/knowledge representation → model services → orchestration → engineer interfaces — all under continuous governance/feedback.
- Concrete AI use cases: build optimization, partitioning, scheduling, timing prediction, debug instrumentation, and failure triage — replacing static heuristics with data-driven decisions.