AI Alone isn't Ready for Chip Design: A Combination of Classical Search and Machine Learning May be the Way Forward

CHIP DESIGN has come a long way since 1971, when Federico Faggin finished sketching the first commercial microprocessor, the Intel 4004, using little more than a straight-edge and colored pencils. Today's designers have a plethora of software tools at their disposal to plan and test new integrated circuits. But as chips have grown staggeringly complex—with some comprising hundreds of billions of transistors—so have the problems designers must solve. And those tools aren't always up to the task. ■ Modern chip engineering is an iterative process of nine stages, from system specification to packaging. Each stage has several substages, and each of those can take weeks to months, depending on the size of the problem and its constraints. Many design problems have only a handful of viable solutions out of 10¹⁰⁰ to 10¹⁰⁰⁰ possibilities—a needle-in-a-hay-stack scenario if ever there was one. Automation tools in use today often fail to solve real-world problems at this scale, which means that humans must step in, making the process more laborious and time-consuming than chipmakers would like. ■ Not surprisingly, there is a growing interest in using machine learning to speed up chip design. However, as our team at the Intel AI Lab has found, machine-learning algorithms are often insufficient on their own, particularly when dealing with multiple constraints that must be satisfied.