47092078
A study of netlist structure and placement efficiency
In this paper, we examine the relationship between netlist structure and the efficiency of placers measured in terms of quality and stability of results. We analyze three types of placers: analytic, simulated-annealing based, and partition based. We have tested these placers on industrial and synthetic benchmarks. Based on our observations and analyzes of experimental results, we draw several useful conclusions: 1) Various placers favor netlists with different structural characteristics; 2) placement efficiency correlates with interconnection complexity of the circuits; 3) global nets increase the effort to eliminate overlaps of cells and degrade the efficiency of analytic placers; and 4) global nets can improve placement stability of partition-based placers.
17071580
Pre-layout physical connectivity prediction with application in clustering-based placement
In this paper, we introduce a structural metric, logic contraction, for pre-layout physical connectivity prediction. For a given set of nodes forming a cluster in a netlist, we can predict their proximity in the final layout based on the logic contraction value of the cluster. We demonstrate a very good correlation of our pre-layout measure with the post-layout physical distances between those nodes. We show an application of the logic contraction to circuit clustering. We compare our seed-growth clustering algorithm with the existing efficient clustering techniques. Experimental results demonstrate the effectiveness of our new clustering method.
10460404
Pre-layout wire length and congestion estimation
In this paper, we study the pre-layout wire length and congestion estimation. We find that two structural metrics, mutual contraction and net range, can be used to predict wire lengths. These metrics have different application ranges and complement each other. We also propose a new metric, the structural pin density, to capture the peak routing congestion of designs. Larger maximum pin densities usually lead to larger peak congestions in circuits with similar average congestions. We demonstrate experimentally very good correlation of our pre-layout measures with post layout interconnect lengths. We also isolate the structural netlist properties which cause the peak congestion.
8549309
Wire length prediction in constraint driven placement
Experiments show that lengths of individual wires are different for different placement algorithms. To achieve accurate wire length prediction, some knowledge of a placer's details is necessary. We postulate that wire length prediction should be coupled with placement flow to obtain accurate results. In this paper, we embed wire length prediction into our constraint-driven placer, developed in Fast Placer Implementation (FPI) framework [15]. We predict individual wire lengths during the clustering step. The predicted wire lengths act as constraints for the simulated annealing refinement stage, which guides the placement towards a solution fulfilling the predictions. Experimental results show that our wire length prediction process yields accurate results without quality loss at a small cost of placement effort. This is the first time that constraints have been used to guide placement and thus increase the accuracy of wire length prediction.
677313
A study of netlist structure and placement efficiency
In this paper, we examine the relationship between netlist structure and the efficiency of placers measured in terms of quality and stability of results. We analyze three types of placers: analytic, simulated-annealing based, and partition based. We have tested these placers on industrial and synthetic benchmarks. Based on our observations and analyzes of experimental results, we draw several useful conclusions: 1) Various placers favor netlists with different structural characteristics; 2) placement efficiency correlates with interconnection complexity of the circuits; 3) global nets increase the effort to eliminate overlaps of cells and degrade the efficiency of analytic placers; and 4) global nets can improve placement stability of partition-based placers.
15497881
A congestion-driven placement framework with local congestion prediction
In this paper, we present a novel congestion-driven placement methodology. We analyze netlist structure and perform local congestion prediction to determine the amount of white space which will be attached to each cell. We place the modified netlist using a total-wire-length-driven placer and obtain an initial legal placement. We extract the congestion information and determine the target white space distribution. Finally, we apply placement migration, an incremental placement technique, to transform the initial placement into a placement which satisfies the target white space distribution. We provide experimental data to demonstrate the effectiveness of our congestion driven placement framework. Compared with the recent academic routability-driven placer Dragon [12], we achieve significant routability improvements in much shorter cpu run time.
3992721
Error minimization in approximate range aggregates
Histogram techniques have been used in many commercial database management systems to estimate a query result size. Recently, it has been shown that they are very effective to support approximation of query processing especially aggregates. In this paper, we investigate the problem of minimizing average errors of approximate aggregates using histogram techniques. Firstly, we present a novel linear-spline histogram model that is more accurate than the existing models. Secondly, we propose a novel histogram construction technique for minimizing such average errors, which is shown to generate a near optimal histogram. Our experiment results demonstrate that the new histogram construction techniques lead to a great accuracy improvement on the existing techniques.
2285434
Towards multidimensional subspace skyline analysis
The skyline operator is important for multicriteria decision-making applications. Although many recent studies developed efficient methods to compute skyline objects in a given space, none of them considers skylines in multiple subspaces simultaneously. More importantly, the fundamental problem on the semantics of skylines remains open: Why and in which subspaces is (or is not) an object in the skyline? Practically, users may also be interested in the skylines in any subspaces. Then, what is the relationship between the skylines in the subspaces and those in the super-spaces? How can we effectively analyze the subspace skylines? Can we efficiently compute skylines in various subspaces and answer various analytical queries?In this article, we tackle the problem of multidimensional subspace skyline computation and analysis. We explore skylines in subspaces. First, we propose the concept of Skycube, which consists of skylines of all possible nonempty subspaces of a given full space. Once a Skycube is materialized, any subspace skyline queries can be answered online. However, Skycube cannot fully address the semantic concerns and may contain redundant information. To tackle the problem, we introduce a novel notion of skyline group which essentially is a group of objects that coincide in the skylines of some subspaces. We identify the decisive subspaces that qualify skyline groups in the subspace skylines. The new notions concisely capture the semantics and the structures of skylines in various subspaces. Multidimensional roll-up and drill-down analysis is introduced. We also develop efficient algorithms to compute Skycube, skyline groups and their decisive subspaces. A systematic performance study using both real data sets and synthetic data sets is reported to evaluate our approach.
12061046
Parsing the Penn Chinese Treebank with Semantic Knowledge
We build a class-based selection preference sub-model to incorporate external semantic knowledge from two Chinese electronic semantic dictionaries. This sub-model is combined with modifier-head generation sub-model. After being optimized on the held out data by the EM algorithm, our improved parser achieves 79.4% (F1 measure), as well as a 4.4% relative decrease in error rate on the Penn Chinese Treebank (CTB). Further analysis of performance improvement indicates that semantic knowledge is helpful for nominal compounds, coordination, and N⋄V tagging disambiguation, as well as alleviating the sparseness of information available in treebank.
5635390
Image Complexity and Feature Extraction for Steganalysis of LSB Matching Steganography
In this paper, we present a scheme for steganalysis of LSB matching steganography based on feature extraction and pattern recognition techniques. Shape parameter of generalized Gaussian distribution (GGD) in the wavelet domain is introduced to measure image complexity. Several statistical pattern recognition algorithms are applied to train and classify the feature sets. Comparison of our method and others indicates our method is highly competitive. It is highly efficient for color image steganalysis. It is also efficient for grayscale steganalysis in the low image complexity domain
45863395
First Order Rough Logic-Revisited
Based on axiomatic rough set theory, first order rough logic was developed earlier. In this paper, a new model theory for that logic is introduced. With this new semantic, first order rough logic is shown to be equivalent to first order S5, and hence consistent and complete.