Anna Goldie

Anna Goldie designs computer chips using reinforcement learning, an AI technique that works by repeatedly generating solutions from an artificial neural network. The system then provides feedback to the network, “reinforcing” pathways that lead to successful outcomes and weakening pathways that don’t. 

Building on this branch of machine learning, which also underlies the most successful methods for teaching computers to play games like chess or Go, has allowed Goldie and her team to speed up the process of chip design.

Modern chips are composed of millions or even billions of components. Some perform computations; others store data in short-term memory. Figuring out the best way to place all the components in a chip’s layout can take engineers weeks or even months—they must try to minimize power consumption and area but also maximize performance, all while making sure that traffic between components doesn’t get too congested. 

Goldie’s AI can, in under six hours, come up with solutions that match—or even outperform—the ones that people were able to develop.

In early 2021, Goldie collaborated with Google engineers to produce physical versions of her layouts for Google’s latest artificial-intelligence chip. By using AI to design better hardware faster, she hopes to pave the way for AI advances that further improve and accelerate hardware design, creating a symbiotic loop between hardware and artificial intelligence.

“It generates these very strange, alien-looking layouts,” she says. “The chip designers were like: What if it goes wrong?” It didn’t.