E100 - Vibe RTL (Tzviel Lemberger)

The AI revolution in hardware development shifts the engineering paradigm from manual, tool-assisted implementation to intent-based workflows. By leveraging machine learning and generative AI, engineers can now explore vast design spaces, optimize Power, Performance, and Area (PPA) metrics, and automate complex verification cycles that were previously time-prohibitive. While Large Language Models (LLMs) excel at processing unstructured data, reinforcement learning remains superior for tasks requiring precise graphical connections, such as place and route. The integration of AI into the silicon lifecycle necessitates a fundamental change in methodology, moving away from human-centric, step-by-step execution toward autonomous agent-driven processes. Despite these advancements, human oversight remains critical for defining design intent, ensuring safety, and managing the inherent risks of complex chip production where failure is not an option.