Machine learning is increasingly being deployed across a wide swath of chips and electronics in automobiles, both for improving reliability of standard parts and for the creation of extremely complex AI chips used in increasingly autonomous applications.

On the design side, the majority of EDA tools today rely on reinforcement learning, a machine learning subset of AI that teaches a machine how to perform a specific task based on pattern recognition. Unlike image recognition in AI chips, which is based on training of massive data sets, machine learning can produce accurate results quickly using much smaller volumes of data. Synopsys, Cadence, Siemens, and others all have embraced reinforcement learning in their tools, and their automotive customers point to improved time to market for chips that offer better performance and meet stringent safety goals.