10166803
An-OARSMan: obstacle-avoiding routing tree construction with good length performance
Routing is one of the important steps in VLSI/ULSI physical design. The rectilinear Steiner minimum tree (RSMT) construction is an essential part of routing. Since macro cells, IP blocks, and pre-routed nets are often regarded as obstacles in the routing phase, obstacle-avoiding RSMT (OARSMT) algorithms are useful for practical routing applications. This paper focuses on the OARSMT problem and presents an algorithm, named An-OARSMan, based on ant colony optimization. A greedy obstacle penalty distance (OP-distance) local heuristic is used in the algorithm and performed on the track graph. The algorithm has been implemented and tested on different kinds of obstacles. Experimental results show that An-OARSMan can handle complex obstacle cases including both convex and concave polygon obstacles with good length performance. It can always achieve the optimal solution in the cases with no more than 7 terminals.
22875771
ACO-Steiner: Ant Colony Optimization Based Rectilinear Steiner Minimal Tree Algorithm
The rectilinear Steiner minimal tree (RSMT) problem is one of the fundamental problems in physical design, especially in routing, which is known to be NP-complete. This paper presents an algorithm, called ACO-Steiner, for RSMT construction based on ant colony optimization (ACO). An RSMT is constructed with ants' movements in Hanan grid, and then the constraint of Hanan grid is broken to accelerate ants' movements to improve the performance of the algorithm. This algorithm has been implemented on a Sun workstation with Unix operating system and the results have been compared with the fastest exact RSMT algorithm, GeoSteiner 3.1 and a recent heuristic using batched greedy triple construction (BGTC). Experimental results show that ACO-Steiner can get a short running time and keep the high performance. Furthermore, it is also found that the ACO-Steiner can be easily extended to be used to some other problems, such as rectilinear Steiner minimal tree avoiding obstacles, and congestion reduction in global routing.
10466292
Via-aware global routing for good VLSI manufacturability and high yield
CAD tools have become more and more important for integrated circuit (IC) design since a complicated system can be designed into a single chip, called system-on-a-chip (SOC), in which physical design tool is an essential and critical part. We try to consider the via minimization problem as early as possible in physical design. We propose a routing method focusing on minimizing vias while considering mutability and wire-length constraint. That is, in the global routing phase, we minimize the number of bends, which is closely related to the number of vias. Previous work only dealt with very small nets, but our algorithm is general for the nets with any size. Experimental results show that our algorithm can greatly reduce the count of bends for various sizes of nets while meeting the constraints of congestion and wire-length.
30531140
A Comparative Study of Several Cluster Number Selection Criteria
The selection of the number of clusters is an important and challenging issue in cluster analysis. In this paper we perform an experimental comparison of several criteria for determining the number of clusters based on Gaussian mixture model. The criteria that we consider include Akaike’s information criterion (AIC), the consistent Akaike’s information criterion (CAIC), the minimum description length (MDL) criterion which formally coincides with the Bayesian inference criterion (BIC), and two model selection methods driven from Bayesian Ying-Yang (BYY) harmony learning: harmony empirical learning criterion (BYY-HEC) and harmony data smoothing criterion (BYY-HDS). We investigate these methods on synthetic data sets of different sample size and the iris data set. The results of experiments illustrate that BYY-HDS has the best overall success rate and obviously outperforms other methods for small sample size. CAIC and MDL tend to underestimate the number of clusters, while AIC and BYY-HEC tend to overestimate the number of clusters especially in the case of small sample size.
11295819
Biomedical Ontology MeSH Improves Document Clustering Qualify on MEDLINE Articles: A Comparison Study
Document clustering has been used for better document retrieval, document browsing, and text mining. In this paper, we investigate if biomedical ontology MeSH improves the clustering quality for MEDLINE articles. For this investigation, we perform a comprehensive comparison study of various document clustering approaches such as hierarchical clustering methods (single-link, complete-link, and complete link), bisecting K-means, K-means, and suffix tree clustering (STC) in terms of efficiency, effectiveness, and scalability. According to our experiment results, biomedical ontology MeSH significantly enhances clustering quality on biomedical documents. In addition, our results show that decent document clustering approaches, such as bisecting K-means, K-means and STC, gains some benefit from MeSH ontology while hierarchical algorithms showing the poorest clustering quality do not reap the benefit of MeSH ontology
38024133
A Comparative Study on Selection of Cluster Number and Local Subspace Dimension in the Mixture PCA Models
How to determine the number of clusters and the dimensions of local principal subspaces is an important and challenging problem in various applications. Based on a probabilistic model of local PCA, this problem can be solved by one of existing statistical model selection criteria in a two-phase procedure. However, such a two-phase procedure is too time-consuming especially when there is no prior knowledge. The BYY harmony learning has provided a promising mechanism to make automatic model selection in parallel with parameter learning. This paper investigates the BYY harmony learning with automatic model selection on a mixture PCA model in comparison with three typical model selection criteria: AIC, CAIC, and MDL. This comparative study is made by experiments for different model selection tasks on simulated data sets under different conditions. Experiments have shown that automatic model selection by the BYY harmony learning are not only as good as or even better than conventional methods in terms of performances, but also considerably supervisory in terms of much less computational cost.
28021583
The Human Use Regulatory Affairs Advisor: A Web Based Information Retrieval System with a User-Friendly Interface Design
12292456
The Embodiment of Amodal Symbolic Knowledge Representations
Latent semantic analysis (LSA) is a statistical, corpus-based technique of representing knowledge. It has been successfully used in a variety of applications including intelligent tutoring systems, essay grading and coherence metrics. From its very introduction LSA has been claimed to simulate aspects of human knowledge representation. This amodal symbolic view immediately resulted in a rebuttal from cognitive scientists who argued that LSA can never come close to human knowledge representation, because it lacks embodiment into perceptual experience or action as in human cognition. The ultimate test to determine the embodiment of LSA is to evaluate how well it operates on typical embodied dimensions like spatiality and temporality. The results of such a test would have an impact on theories of meaning and knowledge representation, and would help us in the development of natural language processing modules for applications that use these embodied dimensions. Six Multidimensional Scaling representations are derived from the LSA cosines between spatial and temporal words. Results show that by combining LSA with MDS previously hidden spatial and temporal relations can now be revealed.