Presentation
LLMs Meet Post-Silicon Test Engineering: A New Era
DescriptionThe transformative power of Large Language Models (LLMs) is reshaping the role of AI in post-silicon test engineering. Recent advancements in LLMs showcase their remarkable ability to engage in diverse dialogues, reason about tasks, and generate code, unlocking new possibilities for automation and efficiency. This talk explores our experience in leveraging LLMs to develop an AI agent tailored for post-silicon test engineering. Central to our approach is the adoption of a natural language programming paradigm, enabling the LLM to reason effectively within a specific domain context. At the core of our AI agent lies a novel two-stage grounding process. First, we utilize the in-context learning capabilities of the LLM to interpret tasks, and second, we validate and refine its responses using a pre-defined knowledge graph. This grounding ensures seamless integration of the LLM with existing test engineering infrastructure, empowering the AI agent to autonomously execute tasks within the established framework. Using the Intelligent Engineering Assistant (IEA) as a case study, we demonstrate how LLM-powered domain-specific AI agents can automate key aspects of test engineering. We will share experimental results from multiple product lines, illustrating the feasibility and impact of deploying IEA in an industrial environment. This talk aims to highlight the potential of LLMs to revolutionize post-silicon test engineering by enabling intelligent, context-aware automation
Event Type
Research Special Session
TimeMonday, June 2311:30am - 12:00pm PDT
Location3010, Level 3
AI