The Golden Age of machine learning is upon EDA. Over the past four years, we have seen large EDA suppliers and customers grow their internal ML teams and strategies, and ML research projects are emerging in all areas of EDA. But, we have not yet seen much of this investment convert into real production flows and work. This talk reviews a set of challenges that make it difficult to bring ML solutions to production for semiconductor design, and discusses approaches for solving them. We will discuss how these approaches are already benefiting variation-aware design and characterization flows, and the broader applicability to analog and digital verification.

Machine Learning, Deep Learning and AI are a technology that lots of enterprise are using to provide smarter services to their customer. Usually, data are acquired by sensors and then sent to a cloud or a remote server to perform inference and get the results. This is no longer the only way, in fact, commercial products are already available to offload the execution of ML and deep-Learning algorithms from the CPU of small devices. In this talk we will analyse some scenarios in which these accelerators, like Neural Compute Stick, can be useful in meeting design goals and allow the realization of smarter edge device.