{"id":19380,"date":"2025-06-03T15:45:54","date_gmt":"2025-06-03T19:45:54","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/verificationhorizons\/?p=19380"},"modified":"2026-03-27T08:53:41","modified_gmt":"2026-03-27T12:53:41","slug":"from-rule-based-beginnings-to-ai-driven-design-tracing-the-evolution-of-ai-in-eda","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/verificationhorizons\/2025\/06\/03\/from-rule-based-beginnings-to-ai-driven-design-tracing-the-evolution-of-ai-in-eda\/","title":{"rendered":"From Rule-Based Beginnings to AI-Driven Design: Tracing the Evolution of AI in EDA"},"content":{"rendered":"\n

As we gear up for the 62nd Design Automation Conference (DAC)<\/a> in San Francisco, one of the most anticipated events is the Accellera-sponsored luncheon panel<\/strong>:<\/p>\n\n\n\n

Can AI Cut Costs in Electronic Design & Verification While Accelerating Time-To-Market?<\/a><\/p>\n\n\n\n

This panel brings together voices from across the industry to examine how artificial intelligence is reshaping the design and verification landscape. As one of the panelists, I\u2019m excited not just to talk about the future\u2014but to reflect on how we got here.<\/p>\n\n\n\n

To understand where AI in EDA is headed, it helps to take a step back and look at how far we\u2019ve come.<\/p>\n\n\n\n

The Genesis: Silicon Compilers Inc.<\/strong><\/p>\n\n\n\n

In 1981, a bold new company called Silicon Compilers, Inc. (SCI)<\/strong> set out to automate the design of custom ICs using high-level specifications. The company\u2019s work culminated in the development of early silicon compiler technology, which translated behavioral descriptions directly into chip layouts\u2014a revolutionary idea at the time.<\/p>\n\n\n\n

SCI\u2019s tools relied on symbolic AI techniques, particularly rule-based expert systems, to encode human design knowledge into automated flows. While these early efforts didn\u2019t involve learning-based AI, they marked the first serious attempt to commercialize AI-inspired automation in EDA and laid the groundwork for decades of innovation to follow.<\/p>\n\n\n\n

The Evolution: From Symbolic AI to Statistical Learning<\/strong><\/p>\n\n\n\n

Over the following decades, the industry saw incremental\u2014but important\u2014advances in applying AI to EDA. The 1990s and early 2000s brought experimentation with heuristic search, evolutionary algorithms, and simulated annealing for tasks like placement and routing. However, these methods often struggled with adaptability, frequently requiring significant hand-tuning to remain effective across new designs.<\/p>\n\n\n\n

The real shift began with the rise of machine learning and data-driven modeling. As compute power and available design data scaled, EDA innovators began applying statistical techniques to extract actionable insights from large, complex datasets\u2014unlocking new ways to optimize everything from synthesis to simulation.<\/p>\n\n\n\n

A Milestone: Solido Design Automation<\/strong><\/p>\n\n\n\n

A major breakthrough came with the founding of Solido Design Automation<\/strong> in 2005. Solido pioneered the application of machine learning and active learning to analog, RF, and custom digital design. Its Variation Designer<\/strong> and ML Characterization Suite<\/strong> used AI to intelligently model circuit behavior across large process, voltage, and temperature (PVT) spaces\u2014dramatically reducing the number of required SPICE simulations.<\/p>\n\n\n\n

These solutions went beyond automation\u2014they learned and adapted. And they delivered real, measurable ROI in both design quality and turnaround time. Solido\u2019s success proved that AI could be more than just a research curiosity\u2014it could be production-proven, trusted, and transformative.<\/p>\n\n\n\n

Recognizing this potential, Siemens acquired Solido in 2017, integrating its powerful modeling capabilities into our broader EDA portfolio.<\/p>\n\n\n\n

The Present: AI at the Forefront<\/strong><\/p>\n\n\n\n

Today, AI is no longer a side experiment\u2014it\u2019s central to modern EDA. At Siemens, we continue to advance the frontier on multiple fronts.<\/p>\n\n\n\n

The Solido Design Environment uses machine learning to achieve SPICE-accurate coverage with a fraction of the simulation effort, accelerating design closure across analog and mixed-signal domains.<\/p>\n\n\n\n

In the digital space, we recently introduced Questa\u2122 One<\/a><\/strong>, our smart functional verification solution. Questa One brings together AI-powered automation, a unified simulation engine, and data-driven analytics to tackle the growing verification productivity gap. It\u2019s engineered to deliver faster engines, faster engineers, and fewer workloads\u2014enabling teams to scale with complexity while improving confidence and time-to-market.<\/p>\n\n\n\n

These aren\u2019t just incremental upgrades. They represent a fundamental rethinking of how we approach design and verification in the age of AI.<\/p>\n\n\n\n

Looking Ahead: Join Us at DAC 2025<\/strong><\/p>\n\n\n\n

The Accellera luncheon panel at DAC 2025<\/strong> will explore how AI is impacting cost, schedule, and quality across the electronics industry. As a panelist, I\u2019m looking forward to a lively and honest discussion\u2014not only about what’s possible today, but where we see AI taking us next.<\/p>\n\n\n\n

\ud83d\udcc5 Tuesday, June 24, 2025<\/strong>
\ud83d\udd5b 12:00 \u2013 1:30 PM PDT<\/strong>
\ud83d\udccd Room 2006 | Moscone West | San Francisco, CA<\/strong><\/p>\n\n\n\n

We\u2019ll explore real-world use cases, separate hype from reality, and examine how standards, ethics, and trust will shape AI\u2019s role going forward. This is one panel you won\u2019t want to miss.<\/p>\n\n\n\n

\ud83d\udd17 Full details and registration<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

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