Data-driven CAD or Algorithm-Driven CAD: Competitors or Collaborators?

Motivation: Despite decades of R&D in algorithm-driven CAD, the design and implementation of SoCs requires an ever-increasing number of resources in terms of designers, compute servers and tool licenses. Design automation has not scaled with the complexity of deep sub-micron fabrication process or the complexity of optimizing power, performance and area (PPA) of modern SoCs. There seems to be a fundamental limit to algorithm-driven CAD that prevents tools from scaling to meet the increasing complexity. As technology scaling reaches its limits and the PPA gains from technology scaling get limited, the need for design tools to close PPA gap through design will increase significantly, making this problem worse. Problem statement: Approach:SoC design consists of taking a chip hardware spec and generating the fabrication mask spec, involving two main tasks: (1) logic synthesis and (2) physical design. While algorithm-driven CAD tools exist to automate both these tasks, they cannot meet the PPA without a large number of manually guided design iterations that consume manpower, compute and tool resources. Approach: Data-driven CAD can capture the learning from manual PPA optimization, and data-driven tools inherently scale with design complexity. We explore the open problems in using Data-driven CAD, to complement the automation capabilities of algorithm-driven CAD and meet the increasing PPA demands of modern SOCs in deep-submicron technologies.