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The AI-hype started a few years ago, with advances in object recognition. Soon the EDA research community made proposals on applying AI in EDA and all major players announced new AI-based tools at DAC 2018. Unfortunately, few new AI-based EDA-tools made it to productive use today. This talk analyses general challenges of AI in EDA, outlines promising use cases, and motivates more AI research in EDA: More HI (=Human Intelligence) is needed to make AI successful in EDA.Motivation: For a long time, hardware design resides in an area between hell of complexity and hell of physics. Continuously decreasing feature size enables to put more and more transistors on a square millimeter silicon. This offers to make continuously new applications at a reasonable form factor. However, the functionality of the application must be designed first and the deep submicron effects must be considered properly.EDA tools help to automate design, but face challenges keeping up with the continuously increasing productivity demand. Therefore, the design teams have increased in size to step up the design of the chips. So, any further innovation is welcome. The re-discovery of AI in general and ML in particular created visions of learning from designers and automatically creating automation from these learnings. To give an example, Google describes in [1] how to accelerate Chip Placement from weeks to hours. |
