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

Abstract

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 pairwise 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-theart placers.

Analytical Clustering Score with Application to Post-Placement Multi-Bit Flip-Flop Merging

“…4-bit in our library). Then we perform further refinement to improve the ratio of 4-bit clusters in the second pass (in line [12][13][14][15][16][17][18][19][20]. It is necessary since there will be some 2-bit or 3-bit clusters after the first pass.…”

“…There are two categories of clustering problems: one is for circuit size reduction, where a short survey can be found in [20]; the other is for design-specific Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored.…”

BonnPlace

“…Location-oriented clustering has previously been used by the algebraic multigrid solver [8], mFAR [14], FastPlace 3.0 [27] and SafeChoice [29].…”

“…In [8], another greedy algorithm iteratively clusters circuits depending on current cell positions and cell sizes only, and thus regarding cell connectivity only implicitly. The concept of SafeChoice [29] is to reject clusters that would degrade optimum netlength. Practically, they also use BestChoice with a modified clustering score function that trades off safety against size of the arising clusters.…”

POLAR: A High Performance Mixed-Size Wirelengh-Driven Placer With Density Constraints

“…And the placement density constraints are smoothed by differentiable nonlinear function, e.g., bell-shaped function [3] and inverse Laplace transformation [4]. Because solving nonlinear programming is time consuming, nonlinear placers usually apply a multilevel approach [5], [6] to reduce runtime. Examples of nonlinear placers are APlace [7], mPL [4], and NTUPlace [8].…”