“It’s more about how we can use the knowledge we’ve gained from process manufacturing data to influence some of the design decisions we make that would then optimize the overall design space,” said Banerjee, representing the test side of the business.
Farkash highlighted knowledge as a key challenge, as well as an opportunity. Afterall, applying AI to different chip design solutions requires a deep understanding of how it all works. At the same time, it also presents a chance to explore where AI and machine learning can be applied. “Wherever you look, it’s just opportunity,” she said. “It’s everywhere.”
Agrawal noted that, had he been asked the same question six months ago, he would’ve had a different answer. However, he said, ChatGPT has proven that AI is ahead of what we’ve expected. “What else can AI do?” Agrawal asked. “To me the sky’s the limit. Whatever I say today, it’ll be wrong in two to three years!” The challenge, he said, is to find talented people who are interested and motivated to work in EDA, especially as AI touches so many areas, and to optimize the compute platform for EDA algorithms.
Another challenge for the EDA industry in particular is, it doesn’t have an unlimited amount of data for AI training, Andersen noted. To overcome this, Synopsys has focused on applying reinforcement learning to actual designs on the fly, eliminating the need to pretrain on data. Another obstacle are the naysayers who are skeptical at how AI could arrive at better results than they can, but those who adopt AI are the ones who will ultimately be successful, Andersen said. In fact, AI also presents a solution to knowledge gaps that occur when people leave an organization, he added.