SafeChoice: A Novel Approach to Hypergraph Clustering for Wirelength-Driven Placement

Abstract

This paper presents a completely new approach to the problem of hypergraph clustering for wirelength-driven placement. The novel algorithm we propose is called SafeChoice (SC). Different from all previous approaches, SC is proposed based on a fundamental theorem, safe condition which guarantees that clustering would not degrade the placement wirelength. To mathematically derive such a theorem, we first introduce the concept of safe clustering, i.e., do clustering without degrading the placement quality. To efficiently check the safe condition for pair-wise clustering, we propose a technique called selective enumeration. SafeChoice maintains a global priority queue based on the safeness and area of potential clusters. Using a simple heuristic, it automatically stops clustering when generating more clusters would degrade the placement wirelength. Moreover, we extend SafeChoice to do clustering while considering the object physical locations, i.e., physical clustering. Finally, we apply SafeChoice into a two-phase placement framework and propose a high-quality analytical placement algorithm called SCPlace. Comprehensive experimental results show that the clusters produced by SC consistently help the placer to achieve the best wirelength among all other clustering algorithms, and SCPlace generates the best half-perimeter wirelength compared with all other state-of-the-art placers.