MADISON, Wis. — Semiconductor engineers are already hearing AI’s footsteps as it encroaches on their design work.
Consider the vast amount of design data and variability required of chip designers, especially when developing a variety of chips with different power, temperature and performance specs. Complex IC designs might well be one of the logical areas to apply machine learning.
At least one EDA software company is making headway with home-grown machine-learning algorithms — calling it “machine learning for engineering” and applying it to variation-aware design and characterization software.
That company is Solido Design Automation, a privately-held EDA software vendor founded in 2005 in Saskatoon, Canada.
Solido has just become the first vendor to make commercially available its machine-learning algorithms to semiconductor customers, by launching Machine Learning (ML) Labs. Solido’s plan is to “collaboratively work with semiconductor companies to develop new ML-based EDA products,” according to the company.
President and CEO Amit Gupta told us, “We’d like to cast a wider net,” to work with more customers to explore, select and apply best ML technologies to solve specific problems.
12 years in Machine Learning
Solido is neither neophyte nor imitator when it comes to machine learning. Gupta explained that his company has been working on machine-learning algorithms for12 years, applying the technology to its flagship product.
Solido is confident of its technical chops. Its machine-learning technologies have been tested, proven in the company’s own commercial product (“Variation Designer,” launched in 2007), and backed by real-world users (1,000 designers and 35 major companies worldwide). Solido, says Gupta, already has seven lead customers committed to working at ML Labs and eager to put Solido’s expertise to use in other areas of EDA.
While declining to name names, Gupta indicated that Solido’s key clients range from a foundry to IDMs and fabless chip companies.
Why ML Labs? (Source: Solido Design Automation)
The idea of machine learning coming to chip design should come as no surprise, especially in the semiconductor industry. Major companies in the semiconductor ecosystem, foundries and EDA companies included, have been quietly investigating how best to apply machine learning to complex chip designs, development and testing to increase the efficiency and accuracy of both design and production.
Laurie Balch, chief analyst at Gary Smith EDA, acknowledged, “EDA tool companies are dabbling in [machine learning].” But she added, “I wouldn’t say they are doing it in such a vast way [throughout the EDA tool chain]. At least not yet.”
Partly, machine learning poses a tricky problem for EDA companies because they must deal with complex design data in vast variety. “To come up with good rules that produce repeatable results is a thorny issue,” said Balch.
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