EDA’s love affair with neural networks is cemented by elective affinities and made unique by the depth and breadth of the challenges
From logic synthesis down to the post-tapeout flow, machine learning has already made inroads in a wide range of EDA tools, enabling shorter turnaround times for chip designs, improving PPA results, and reducing the need for hardware resources across the design cycle. While disruptive advancements are all but unexpected when neural networks come into play, there seems to be something unique in the relationship between Electronic Design Automation and machine learning. On the one hand, it looks like the EDA industry is particularly well equipped to take advantage of the potential of neural networks; on the other hand, the difficulty and diversity of EDA challenges impose the use of several different machine learning solutions, contributing to a uniquely complex ML-enabled flow.
Solving hard problems is business as usual
One aspect that immediately stands out when addressing this subject is the way EDA experts have approached the innovations brought about by neural networks. While they undoubtedly consider machine learning as a disruptive technology enabling exciting results, on the other hand they see neural networks as a natural continuation of what EDA companies have always been doing: writing advanced software to solve hard problems.
“Machine learning is changing the world of software, not just EDA, it’s the next evolution in algorithms,” says Paul Cunningham, Corporate Vice President and General Manager at Cadence. “Our business is complex software, so the math and the computer science behind neural networks and Bayesian methods, all of these deep complex techniques inside machine learning, this is all just very normal for us. We are doing this all the time anyway, this is the software that we write,” he continues. “We have all the experts, there is no problem for us to write the [machine learning] algorithms just from scratch.”
Paul Cunningham. Credit: Cadence
Another reason why machine learning – despite being such a disruptive technology – can be considered a sort of natural development for EDA is that in many tools the new ML-based algorithms are replacing pre-existing traditional heuristic in a way that is invisible to the user. In these cases, “It’s just as using machine learning as a better heuristic,” says Cunningham. “We may have some expert system inside, some other way to take a decision, some rule-based method, and we are now using a neural network-based method for the heuristic, so the customer has no visibility.”
