Blueprint for Achieving Excellence in EDA using AI: Industry Experts Weigh In
Electronic Design Automation (EDA) is undergoing a significant transformation driven by AI. At the 2025 Siemens EDA User2User Conference in Santa Clara, industry leaders from AMD, AWS, Microsoft, NVIDIA, and Siemens EDA gathered for a critical panel discussion titled ‘Industrial-grade AI in EDA: Challenges, Lessons, and Opportunities.’ Their insights, drawn from real-world experience, highlighted four key insights for companies embracing the EDA AI transformation.
EDA AI agents will augment human capabilities
AI agents in EDA will augment, not substitute, human expertise. For example, AI agents will assist with repetitive tool setups using natural language and provide a productivity boost, but will rely on chip designers’ strategic expertise in following a specific chip design approach. The roadmap envisions a future with a collaborative human-agent ecosystem for addressing complex design challenges. Dan Yu, an AI & ML leader at Siemens EDA, explained: “AI optimizes and accelerates EDA workflows. Think of the human EDA engineer as the pilot, making strategic decisions, while AI acts as the co-pilot, boosting productivity and accelerating time to market.”
EDA data is both the foundation and a major challenge for AI implementation
Data is fundamental for AI in EDA, especially for fine-tuning models or building Retrieval-Augmented Generation (RAG) solutions. However, obtaining well-organized, high-quality EDA data (e.g., RTL/Verilog, sample PDKs, etc.) is a challenging task. Furthermore, EDA data typically remains siloed and underutilized and needs to be aggregated in centralized, multimodal EDA data lakes to unlock a strong data flywheel effect for uncovering critical insights across the entire EDA workflow. At the same time, data security and intellectual property (IP) protection pose significant challenges due to the sensitive nature of design information, necessitating robust security measures from the outset.
Compute infrastructure is a key enabler for the EDA AI transformation
Robust cloud and data center infrastructure is of paramount importance not just for model training or fine-tuning, which may occur occasionally, but it is critical for smooth and continuous inference, which happens daily. Specifically, inference is getting more importance, as reasoning models and agentic systems typically require 10x-20x more compute. Thus, semiconductor companies must make a strategic choice between on-premises capital expenditures and cloud computing, or embrace a hybrid solution. To avoid huge costs of constant model retraining, and the ensure you are using the best-in-class models given the rapid AI advancements, Jeff Dyck, Senior Director of R&D at Siemens EDA, suggested: “RAG is a viable methodology. RAG offers flexibility in utilizing the best available models without the need to retrain for every new model released almost monthly. Furthermore, RAG allows enabling an access control layer to restrict RAG operations to authorized documents, thus providing a structured pathway for controlled access to proprietary data.”
Embrace AI by moving fast while not breaking things
Panelists emphasized the importance of proactive AI experimentation, encouraging the implementation of solutions to identify strengths, weaknesses, and areas for iterative improvement. One of the main challenges is reconciling the AI development mindset of ‘moving fast and breaking things’ with the EDA industry’s principle of ‘do not break things.’ The goal is to ‘move fast but not break things’, and striking this balance means fostering a culture of rapid progress without compromising reliability.
Key takeaways from the EDA AI panel:
• EDA AI will augment human expertise
• Rich EDA data is a bedrock and a roadblock
• Compute infrastructure is a key enabler for EDA AI
• Embrace AI by moving fast but not breaking things
