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In this paper, we propose an effective algorithm flow to handle largescale mixed-size placement. The basic idea is to use floorplanning to guide the placement of objects at the global level. The flow consists of four steps: 1) The objects in the original netlist are clustered into blocks; 2) Floorplanning is performed on the blocks; 3) The blocks are shifted within the chip region to further optimize the wirelength; 4) With big macro locations fixed, incremental placement is applied to place the remaining objects. There are several advantages of handling placement at the global level with a floorplanning technique. First, the problem size can be significantly reduced. Second, exact HPWL can be minimized. Third, precise object distribution can be achieved so that legalization only needs to handle minor overlaps among small objects in a block. Fourth, rotation and various placement constraints on macros can be handled. To demonstrate the effectiveness of this new flow, we implement a high-quality floorplanguided placer called FLOP. We also construct the Modern MixedSize (MMS) placement benchmarks which can effectively represent the complexities of modern mixed-size designs and the challenges faced by modern mixed-size placers. Compared with state-of-the-art mixed-size placers and leading macro placers, experimental results show that FLOP achieves the best wirelength, and easily obtains legal solutions on all circuits.

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.

This paper presents an efficient, scalable and optimal slack-driven shaping algorithm for soft blocks in non-slicing floorplan. The proposed algorithm is called SDS. Different from all previous approaches, SDS is specifically formulated for fixed-outline floorplanning. Given a fixed upper bound on the layout width, SDS minimizes the layout height by only shaping the soft blocks in the design. Iteratively, SDS shapes some soft blocks to minimize the layout height, with the guarantee that the layout width would not exceed the given upper bound. Rather than using some simple heuristic as in previous work, the amount of change on each block is determined by systematically distributing the global total amount of available slack to individual block. During the whole shaping process, the layout height is monotonically reducing, and eventually converges to an optimal solution. We also propose two optimality conditions to check the optimality of a shaping solution. To validate the efficiency and effectiveness of SDS, comprehensive experiments are conducted on MCNC and HB benchmarks. Compared with previous work, SDS is able to achieve the best experimental result with significantly faster runtime.