Advancing Placement

Placement is central to IC physical design: it determines spatial embedding, and hence parasitics and performance. From coarse-to fine-grain, placement is conjointly optimized with logic, performance, clock and power distribution, routability and manufacturability. This paper gives some personal thoughts on futures for placement research in IC physical design. Revisiting placement as optimization prompts a new look at placement requirements, optimization quality, and scalability with resources. Placement must also evolve to meet a growing need for co-optimizations and for cooperation with other design steps. "New" challenges will naturally arise from scaling, both at the end of the 2D scaling roadmap and in the context of future 2.5D/3D/4D integrations. And, the nexus of machine learning and placement optimization will continue to be an area of intense focus for research and practice. In general, placement research is likely to see more flow-scale optimization contexts, open source, benchmarking of progress toward optimality, and attention to translations into real-world practice.