All Software Is Hardware-Dependent


I was lucky in my early career that I found two sets of great mentors. The first happened recently after graduating when I joined the Hilo development team. Members of that team included Phil Moorby, Simon Davdimann, Peter Flake, and others. They all had very different coding personalities, but most importantly, they worked as a team and used good foundational processes. One outcome of that ... » read more

AI Won’t Kill Verification IP, But It Will Redefine It


Key Takeaways AI will enhance, not replace, verification IP by automating test generation and debug. Verification IP’s core value will increasingly lie in trust, accountability, and system-level realism, especially as designs become more complex, multi-die, and security-sensitive. AI shifts verification bottlenecks from execution to specification quality, raising expectations for c... » read more

Beating The Heat In 3D Packages


Key Takeaways: Thermal management is a central design constraint, requiring early, thorough planning. Accurate thermal simulation requires AI-driven adaptive meshing and real-world validation. Innovative STCO strategies can drastically reduce GPU peak temperature. As HPC and AI accelerators push power densities to 1kW and beyond, the heat generated by rapidly switching tran... » read more

Human-Centered Agentic AI Comes To RTL Verification


For decades, productivity gains in electronic design automation (EDA) came from better engines. Faster solvers, higher-capacity simulators, and more scalable formal tools allowed design and verification teams to keep pace as designs grew larger. That model is no longer sufficient. Today’s design and verification bottleneck is not raw tool performance, but the coordination overhead required... » read more

Follow The AI Leader


In the 1980s, a common expression was "nobody ever got fired for buying IBM." It was considered the safe option, long after new technologies had emerged. While it may not have been the most advanced option available, it remained the safe bet. It had an established ecosystem, and it was a known quantity. But who or what is the safe bet when it comes to AI? Who has the necessary data? Who has ... » read more

Using Data And AI More Effectively In EDA


Key Takeaways The data being produced by EDA tools tends to be for human consumption and has weak semantics. Agents are attempting to create actionable information from unstructured data. The Model Context Protocol may provide AI with access to better data. Semiconductor design generates a lot of data, but how much of that is useful or currently being used by AI tools? And h... » read more

Benchmark For AI-Aided Chip Design That Evaluates LLMs Across 3 Critical Tasks (UCSD, Columbia)


Researchers at UCSD and Columbia University published "ChipBench: A Next-Step Benchmark for Evaluating LLM Performance in AI-Aided Chip Design." Abstract "While Large Language Models (LLMs) show significant potential in hardware engineering, current benchmarks suffer from saturation and limited task diversity, failing to reflect LLMs' performance in real industrial workflows. To address t... » read more

How The EDA Industry Will Evolve In 2026


AI will continue to impact every facet of the EDA industry. Pressure will mount in 2026 on design teams to drive productivity gains while technical complexity continues to escalate. This will reshape how teams work and the tools they use. Success will be determined by balancing the trade-offs between integrated platforms and best-of-breed toolchains and developing talent internally rather than ... » read more

EDA and IP Revenue Up 8.8%


EDA and IP revenue grew 8.8% in Q3 2025 to $5.566 billion, up from $5.115 billion in the same period in 2024, according to new data from ESD Alliance. But beneath those respectable, if not spectacular numbers, some interesting shifts are underway. China returned to double-digit growth after several quarters of lackluster sales. But the biggest surprise was EDA/IP revenues from South Korea an... » read more

Transforming Data Management In EDA: Preparing For The AI Era


In today’s fast-paced electronics design automation (EDA) environment, effective data management has become essential. Growing design complexity, distributed teams, and the accelerating adoption of AI/ML are pushing organizations to rethink how they manage, track, and leverage decades of engineering data. From manual workarounds to data management Many engineers discover the importance ... » read more

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