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.
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.
86206
Wire length prediction-based technology mapping and fanout optimization
In modern VLSI systems early wire optimization is essential in achieving required performance, routability, and power. The cost functions optimized during traditional logic synthesis do not capture interconnect effects, which may lead to sub-optimal designs. In this paper, we propose a method for applying pre-layout wire-length prediction techniques in logic synthesis, targeting wiring cost and ways to minimize congestion. Specifically, we focus on technology mapping and fanout optimization. Experimental results show that our wire-length-prediction-based technology mapping (WP-Map) and fanout optimization (WP-Fanout) result in 8.7% improvement in average congestion, 17.2% improvement in peak congestion, and 3.3% improvement in timing performance.
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.
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.
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.
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
6832980
Maximum Entropy Based Phrase Reordering Model for Statistical Machine Translation
We propose a novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model provides content-dependent, hierarchical phrasal reordering with generalization based on features automatically learned from a real-world bitext. We present an algorithm to extract all reordering events of neighbor blocks from bilingual data. In our experiments on Chinese-to-English translation, this MaxEnt-based reordering model obtains significant improvements in BLEU score on the NIST MT-05 and IWSLT-04 tasks.
8754175
Summarizing level-two topological relations in large spatial datasets
Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this article, we present several novel techniques to effectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important level-two topological relations: contains, contained, overlap, and disjoint. We first present a novel framework to construct a multiscale Euler histogram in 2D space with the guarantee of the exact summarization results for aligned windows in constant time. To minimize the storage space in such a multiscale Euler histogram, an approximate algorithm with the approximate ratio 19/12 is presented, while the problem is shown NP-hard generally. To conform to a limited storage space where a multiscale histogram may be allowed to have only k Euler histograms, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy in approximately summarizing aligned windows. Then, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. We also investigate the problem of nonaligned windows and the problem of effectively partitioning the data space to support nonaligned window queries. Finally, we extend our techniques to 3D space. Our extensive experiments against both synthetic and real world datasets demonstrate that the approximate multiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost efficiency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for many popular real datasets.
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.