It turns out this is real, and a lot of people are talking about it. If you want a thought-provoking take on the topic, go to Hot Chips this year. The chairman and co-CEO of Synopsys, Aart de Geus, will be presenting a keynote, Does Artificial Intelligence Require Artificial Architects?
If you want to better understand the forces at play between chip design and AI, I highly recommend this blog post from Stelios Diamantidis. Stelios leads strategy and product management for Synopsys AI and is a founder of the Machine Learning Center of Excellence, where he looks at applying machine learning (ML) technology to key disruptions in the design and manufacturing of integrated computational systems. He’s thought a lot about the interactions between AI and chip design.
He’s also done a few things about it as well. One high-profile application Stelios worked on is the use of AI to learn from prior chip design efforts to consistently achieve better results, in every project. The technology is called design space optimization, or DSO. I’ll take a few words from Stelios’ blog post to explain what it does. He’s way better at explaining it than I am.
One disruptive application of AI in chip design is design space optimization (DSO), a generative optimization paradigm that uses reinforcement-learning technology to autonomously search design spaces for optimal solutions. By applying AI to chip design workflows, DSO facilitates a massive scaling in the exploration of choices while also automating a large volume of less consequential decisions. The approach creates an opportunity for the technology to continuously build on its training data and apply what it has learned to, ultimately, accelerate tapeouts and achieve power, performance, and area (PPA) targets. And one of the key advantages of AI is its support of reuse: the retained learnings gained for one project can be utilized for future projects, bringing greater efficiency into the design process.
The impact of a tool like this is best shown graphically. The figure below depicts a case study to find the lowest power while maintaining total negative slack (TNS). In this case, there was no prior learning. You can see that an automated, AI-guided system can examine many, many data points to converge on a solution that is superior to what an expert human can achieve manually.