Tung-Chieh Chen

NTUplace3: An Analytical Placer for Large-Scale Mixed-Size Designs With Preplaced Blocks and Density Constraints

In addition to wirelength, modern placers need to consider various constraints such as preplaced blocks and density. We propose a high-quality analytical placement algorithm considering wirelength, preplaced blocks, and density based on the log-sum-exp wirelength model proposed by Naylor and the multilevel framework. To handle preplaced blocks, we use a two-stage smoothing technique, i.e., Gaussian smoothing followed by level smoothing, to facilitate block spreading during global placement (GP). The density is controlled by white-space reallocation using partitioning and cut-line shifting during GP and cell sliding during detailed placement. We further use the conjugate gradient method with dynamic step-size control to speed up the GP and macro shifting to find better macro positions. Experimental results show that our placer obtains very high-quality results.

Modern floorplanning based on B/sup */-tree and fast simulated annealing

Unlike classical floorplanning that usually handles only block packing to minimize silicon area, modern very large scale integration (VLSI) floorplanning typically needs to pack blocks within a fixed die (outline), and additionally considers the packing with block positions and interconnect constraints. Floorplanning with bus planning is one of the most challenging modern floorplanning problems because it needs to consider the constraints with interconnect and block positions simultaneously. In this paper, the authors study two types of modern floorplanning problems: 1) fixed-outline floorplanning and 2) bus-driven floorplanning (BDF). This floorplanner uses B/sup */-tree floorplan representation based on fast three-stage simulated annealing (SA) scheme called Fast-SA. For fixed-outline floorplanning, the authors present an adaptive Fast-SA that can dynamically change the weights in the cost function to optimize the wirelength under the outline constraint. Experimental results show that this floorplanner can achieve 100% success rates efficiently for fixed-outline floorplanning with various aspect ratios. For the BDF, the authors explore the feasibility conditions of the B/sup */-tree with the bus constraints, and develop a BDF algorithm based on the conditions and Fast-SA. Experimental results show that this floorplanner obtains much smaller dead space for the floorplanning with hard/soft macro blocks, compared with the most recent work. In particular, this floorplanner is more efficient than the previous works.

A high-quality mixed-size analytical placer considering preplaced blocks and density constraints

In addition to wirelength, modern placers need to consider various constraints such as preplaced blocks and density. We propose a high-quality analytical placement algorithm considering wirelength, preplaced blocks, and density based on the log-sum-exp wirelength model proposed by Naylor et al. (2001) and the multilevel framework. To handle preplaced blocks, we use a two-stage smoothing technique, Gaussian smoothing followed by level smoothing, to facilitate block spreading during global placement. The density is controlled by white-space re-allocation using partitioning and cut-line shifting during global placement and cell sliding during detailed placement. We further use the conjugate gradient method with dynamic step-size control to speed up the global placement and macro shifting to find better macro positions. Experimental results show that our placer obtains the best published results

NTUplace: a ratio partitioning based placement algorithm for large-scale mixed-size designs

In this paper, we present a hierarchical ratio partitioning based placement algorithm for large-scale mixed-size designs. The placement algorithm consists of three steps: global placement, legalization,and detailed placement; it works in a hierarchical manner and integrates net-weighting partitioning, whitespace management, look-ahead bipartitioning, and fast legalization to handle the large-scale mixed-size placement problems. Unlike the traditional partitioning-based technique that is based on balanced partitioning, we apply ratio partitioning in each level. Further, applying the look-ahead bipartitioning technique in each level, we can evaluate the feasibility of the placement for sub-partitions more accurately. Therefore, we can find better ratios for the partitions, leading to easier legalization for the global placement result and finally a better detailed placement solution. Experimental results show the efficiency and effectiveness of our algorithm.

Essential Issues in Analytical Placement Algorithms

The placement problem is to place objects into a fixed die such that no objects overlap with each other and some cost metric (e.g., wirelength) is optimized. Placement is a major step in physical design that has been studied for several decades. Although it is a classical problem, many modern design challenges have reshaped this problem. As a result, the placement problem has attracted much attention recently, and many new algorithms have been developed to handle the emerging design challenges. Modern placement algorithms can be classified into three major categories: simulated annealing, min-cut, and analytical algorithms. According to the recent literature, analytical algorithms typically achieve the best placement quality for large-scale circuit designs. In this paper, therefore, we shall give a systematic and comprehensive survey on the essential issues in analytical placement. This survey starts by dissecting the basic structure of analytical placement. Then, various techniques applied as components of popular analytical placers are studied, and two leading placers are exemplified to show the composition of these techniques into a complete placer. Finally, we point out some research directions for future analytical placement.

MP-Trees: A Packing-Based Macro Placement Algorithm for Modern Mixed-Size Designs

In this paper, we present a new multipacking-tree (MP-tree) representation for macro placements to handle modern mixed-size designs with large macros and high chip utilization rates. Based on binary trees, the MP-tree is very efficient, effective, and flexible for handling macro placements with various constraints. Given a global placement that already considers the areas and the interconnections among standard cells and macros, our MP-tree-based macro placer optimizes macro positions, minimizes the macro displacement from the initial macro positions, and maximizes the area of the chip center for standard-cell placement and routing. Experiments based on the Proceedings of the 2006 International Symposium on Physical Design placement contest benchmarks and Faraday benchmarks show that our macro placer combined with APlace 2.0, Capo 10.2, mPL6, or NTUplace3 for a standard-cell placement outperforms these state-of-the-art academic mixed-size placers alone by large margins in robustness and quality. In addition to wirelength, experiments on four real industrial designs with large macros and high utilization rates show that our method significantly reduces the average half-perimeter wirelength by 35 %, the average routed wirelength by 55 %, and the routing overflows by 13 times compared with Capo 10.2, implying that our macro placer leads to much higher routability.

A New Multilevel Framework for Large-Scale Interconnect-Driven Floorplanning

We present in this paper a new interconnect-driven multilevel floorplanner, called interconnect-driven multilevel-floorplanning framework (IMF), to handle large-scale building-module designs. Unlike the traditional multilevel framework that adopts the ldquoLambda-shapedrdquo framework (inaccurately called the ldquoV-cyclerdquo framework in the literature): bottom-up coarsening followed by top-down uncoarsening, the IMF, in contrast, works in the ldquoV-shapedrdquo manner: top-down uncoarsening (partitioning) followed by bottom-up coarsening (merging). The top-down partitioning stage iteratively partitions the floorplan region based on min-cut bipartitioning with exact net-weight modeling to reduce the number of global interconnections and, thus, the total wirelength. Then, the bottom-up merging stage iteratively applies fixed-outline floorplanning using simulated annealing for all regions and merges two neighboring regions recursively. Experimental results show that the IMF obtains the best published fixed-outline floorplanning results with the smallest average wirelength for the Microelectronics Center of North Carolina/Gigascale Systems Research Center benchmarks. In particular, IMF scales very well as the circuit size increases. The V-shaped multilevel framework outperforms the Lambda-shaped one in the optimization of global circuit effects, such as interconnection and crosstalk optimization, since the V-shaped framework considers the global configuration first and then processes down to local ones level by level, and thus, the global effects can be handled at earlier stages. The V-shaped multilevel framework is general and, thus, can be readily applied to other problems.

Routability-driven placement for hierarchical mixed-size circuit designs

A wirelength-driven placer without considering routability could introduce irresolvable routing-congested placements. Therefore, it is desirable to develop an effective routability-driven placer for modern mixed-size designs employing hierarchical methodologies for faster turnaround time. This paper presents a novel two-stage technique to effectively identify design hierarchies and guide placement for better wirelength and routability. To optimize wirelength and routability simultaneously during placement, a new analytical net-congestion-optimization technique is also proposed. Compared with the participating teams for the 2012 ICCAD Design Hierarchy Aware Routability-driven Placement Contest, our placer can achieve the best quality (both the average overflow and wirelength) and the best overall score (by additionally considering running time).

Metal-density driven placement for cmp variation and routability

In this paper, we propose the first metal-density-driven (MDD) placement algorithm to reduce chemical-mechanical planarization/polishing (CMP) variation and achieve higher routability. To efficiently estimate metal density and thickness, we first apply a probabilistic routing model and then a predictive CMP model to obtain the metal-density map. Based on the metal-density map, we use an analytical placement framework to spread blocks to reduce metal-density variation. Experimental results based on BoxRouter and NTUgr show that our method can effectively reduce the CMP variation. By using our MDD placement, for example, the topography variation can be reduced by up to 38% (23%) and the number of dummy fills can be reduced by up to 14% (8%), compared with those using wirelength-driven (cell-density-driven) placement. The results of our MDD placement can also lead to better routability.

Metal-Density-Driven Placement for CMP Variation and Routability

In this paper, we propose the first metal-density-driven (MDD) placement algorithm to reduce chemical-mechanical planarization/polishing (CMP) variation and achieve higher routability. To efficiently estimate metal density and thickness, we first apply a probabilistic routing model and then a predictive CMP model to obtain the metal-density map. Based on the metal-density map, we use an analytical placement framework to spread blocks to reduce metal-density variation. Experimental results based on BoxRouter and NTUgr show that our method can effectively reduce the CMP variation. By using our MDD placement, for example, the topography variation can be reduced by up to 38% (23%) and the number of dummy fills can be reduced by up to 14% (8%), compared with those using wirelength-driven (cell-density-driven) placement. The results of our MDD placement can also lead to better routability.