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.
8945696
Synthesis and placement flow for gain-based programmable regular fabrics
In this paper we present the Gain-based Logic Block Array (GLA), a new via-programmable regular fabric. GLA is an array of Gain-based Logic Blocks (GLBs). The GLB is a semi-universal logic block designed based on logical effort theory[12]. Customization of the GLBs is provided by programmable vias. To achieve the best performance, appropriate fabric has to be selected from a family of GLAs with different performance-area trade-offs. We describe a synthesis and placement flow which, for a given design to be implemented, allows us to select the best GLA from the candidate family.
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.
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.
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.
3416923
Efficient Computation of the Skyline Cube
Skyline has been proposed as an important operator for multi-criteria decision making, data mining and visualization, and user-preference queries. In this paper. we consider the problem of efficiently computing a SKYCUBE, which consists of skylines of all possible non-empty subsets of a given set of dimensions. While existing skyline computation algorithms can be immediately extended to computing each skyline query independently, such "shared-nothing" algorithms are inefficient. We develop several computation sharing strategies based on effectively identifying the computation dependencies among multiple related skyline queries. Based on these sharing strategies, two novel algorithms, Bottom-Up and Top-Down algorithms, are proposed to compute SKYCUBE efficiently. Finally, our extensive performance evaluations confirm the effectiveness of the sharing strategies. It is shown that new algorithms significantly outperform the naive ones.
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.
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.
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.
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.