Hierarchical whitespace allocation in top-down placement

Increased transistor density in modern commercial ICs typically originates in new manufacturing and defect prevention technologies [15], [16]. Additionally, better utilization of such low-level transistor density may result from improved software that makes fewer assumptions about physical layout in order to reliably automate the design process. In particular, recent layouts tend to have large amounts of whitespace, which is not handled properly by older tools. We observe that a major computational difficulty arises in partitioning-driven top-down placement when regions of a chip lack whitespace. This tightens balance constraints for min-cut partitioning and hampers move-based local-search heuristics such as Fiduccia-Mattheyses. However, the local lack of whitespace is often caused by very unbalanced distribution of whitespace during previous partitioning, and this concern is emphasized in chips with large overall whitespace. This paper focuses on accurate computation of tolerances to ensure smooth operation of common move-based iterative partitioners, while avoiding cell overlaps. We propose a mathematical model of hierarchical whitespace allocation in placement, which results in a simple computation of partitioning tolerance purely from relative whitespace in the block and the number of rows in the block. Partitioning tolerance slowly increases as the placer descends to lower levels, and relative whitespace in all blocks is limited from below (unless partitioners return "illegal" solutions), thus preventing cell overlaps. This facilitates good use of whitespace when it is scarce and prevents very dense regions when large amounts of whitespace are available. Our approach improves the use of the available whitespace during global placement, thus leading to smaller whitespace requirements. Existing techniques, particularly those based on simulated annealing [21], [10], can be applied after global placement to bias whitespace with respect to particular concerns, such as routing congestion, heat dissipation, crosstalk noise and DSM yield improvement.