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.
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.
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.
17618621
Semi-individual wire-length prediction with application to logic synthesis
A new concept for wire-length prediction, the semi-individual wire-length prediction, is introduced. Structural metrics, such as mutual contraction and net range, are used to predict which interconnects have a tendency to be long or short in the final layout. The very good correlation of the prelayout measures with the postlayout interconnect lengths is demonstrated. The prelayout wire-length-prediction techniques are applied in logic synthesis, targeting wiring cost, and congestion minimization. This paper focuses on technology mapping and fan-out optimization. Experiments on LGSyn93 and ITC'99 benchmark suites show that the wire-length-prediction-based technology mapping and fan-out-optimization algorithms produce layout-friendly netlists. After placement and global routing, the netlists yield smaller total wire length, are more routable, and achieve better timing than the netlists obtained using traditional logic-synthesis flow in SIS.
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.
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.
2785368
Tree Based Behavior Monitoring for Adaptive Fraud Detection
The general basis for anomaly detection and fraud detection is pattern recognition. An effective online fraud detection system should be able to discover both known and new attacks as early as possible. The detection process should be self-adjustable to allow the system to deal with the constantly changing nature of online attacks. In this paper, we present an anomaly detection technique based on behavior mining and monitoring that work at both the individual and system level. Frequent pattern tree is utilized to profile the normal behavior adaptively. A novel tree-based pattern matching algorithm is designed to discover individual level anomalies. An algorithm for computing tree similarity is proposed to solve the system level problems. Empirical evaluations of our technique on both synthetic and real-world data show that we can accurately differentiate anomalous behaviors from the profiled normal behavior
8930290
Data mining of gene expression changes in Alzheimer brain
Genome-wide transcription profiling is a powerful technique for studying the enormous complexity of cellular states. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. The data requires care in both pre-processing and the application of data mining techniques. This paper addresses the problem of dealing with microarray data that come from two known classes (Alzheimer and normal). We have applied three separate techniques to discover genes associated with Alzheimer disease (AD). The 67 genes identified in this study included a total of 17 genes that are already known to be associated with Alzheimer's or other neurological diseases. This is higher than any of the previously published Alzheimer's studies. Twenty known genes, not previously associated with the disease, have been identified as well as 30 uncharacterized expressed sequence tags (ESTs). Given the success in identifying genes already associated with AD, we can have some confidence in the involvement of the latter genes and ESTs. From these studies we can attempt to define therapeutic strategies that would prevent the loss of specific components of neuronal function in susceptible patients or be in a position to stimulate the replacement of lost cellular function in damaged neurons. Although our study is based on a relatively small number of patients (four AD and five normal), we think our approach sets the stage for a major step in using gene expression data for disease modeling (i.e. classification and diagnosis). It can also contribute to the future of gene function identification, pathology, toxicogenomics, and pharmacogenomics.
10184967
Tree-to-String Alignment Template for Statistical Machine Translation
We present a novel translation model based on tree-to-string alignment template (TAT) which describes the alignment between a source parse tree and a target string. A TAT is capable of generating both terminals and non-terminals and performing reordering at both low and high levels. The model is linguistically syntax-based because TATs are extracted automatically from word-aligned, source side parsed parallel texts. To translate a source sentence, we first employ a parser to produce a source parse tree and then apply TATs to transform the tree into a target string. Our experiments show that the TAT-based model significantly outperforms Pharaoh, a state-of-the-art decoder for phrase-based models.
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.
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.