14113310
Techniques for improved placement-coupled logic replication
Several recent papers have utilized logic replication driven by placement-level timing analysis for improving clock period (e.g., [1], [8], [18], and [2]). All of these papers demonstrated, through various optimization strategies, the potential of the basic technique of replication. In this paper we propose a number of techniques aimed at more fully realizing this potential within the framework employed in [8]. As reported in [7], there are situations in which the approach of [8] fails to yield significant additional improvement due largely to the effects of reconvergence in the netlist. We suggest the use of rectilinear Steiner arborescence embedding as a tool for overcoming this limitation. We also propose techniques for fanout partitioning and cell relocation which are cognizant of both wirelength and timing impact for improved solution quality. We report the effect of other techniques including new replication cost computation, lower-bounding of achievable clock period, and wirelength estimation. We have implemented and experimented with these techniques in FPGA domain. In many cases we were able to approach a fixed flip-flop lower-bound on achievable clock period. Promising experimental results are reported with average 17.4% (up to 39.9%) delay reduction compared with the timing-driven placement from VPR[16] and average 9.3% (up to 37.2%) reduction compared with the basic fanin tree embedder from [8].
16407614
Requirements-based dynamic metrics in object-oriented systems
Because early design decisions can have a major long term impact on the performance of a system, early evaluation of the high-level architecture can be an important risk mitigation technique. This paper proposes a technique for predicting the volume of data that will flow across a network in a distributed system. The prediction is based upon anticipated execution of scenarios and can be applied at an extremely early stage of the design. It is driven by requirements specifications and captures dynamic metrics by defining typical usage patterns in terms of scenarios. Scenarios are then mapped to architectural components, and dataflow across inter-partition links is estimated. The feasibility of the approach is demonstrated through an experiment in which predicted metrics are compared to runtime measurements.
2872153
2D-profiling: detecting input-dependent branches with a single input data set
Static compilers use profiling to predict run-time program behavior. Generally, this requires multiple input sets to capture wide variations in run-time behavior. This is expensive in terms of resources and compilation time. We introduce a new mechanism, 2D-profiling, which profiles with only one input set and predicts whether the result of the profile would change significantly across multiple input sets. We use 2D-profiling to predict whether a branch's prediction accuracy varies across input sets. The key insight is that if the prediction accuracy of an individual branch varies significantly over a profiling run with one input set, then it is more likely that the prediction accuracy of that branch varies across input sets. We evaluate 2D-profiling with the SPEC CPU 2000 integer benchmarks and show that it can identify input-dependent branches accurately.
4644097
Component-based LDA face description for image retrieval and MPEG-7 standardisation
We propose a method of face description for facial image retrieval from a large data base and for MPEG-7 (Moving Picture Experts Group) standardisation. The novel descriptor is obtained by decomposing a face image into several components and then combining the component features. The decomposition combined with LDA (Linear Discriminant Analysis) provides discriminative facial descriptions that are less sensitive to light and pose changes. Each facial component is represented in its Fisher space and another LDA is then applied to compactly combine the features of the components. To enhance retrieval accuracy further, a simple pose classification and transformation technique is performed, followed by recursive matching. Our algorithm has been developed to deal with the problem of face image retrieval from huge databases such as those found in Internet environments. Such retrieval requires a compact face representation which has robust recognition performance under lighting and pose variations. The partitioning of a face image into components offers a number of benefits that facilitate the development of an efficient and robust face retrieval algorithm. Variation in image statistics due to pose and/or illumination changes within each component region can be simplified and more easily captured by a linear encoding than that of the whole image. So an LDA encoding at the component level facilitates better classification. Furthermore, a facial component can be weighted according to its importance. The component with a large variation is weighted less in the matching stage to yield a more reliable decision. The experimental results obtained on the MPEG-7 data set show an impressive accuracy of our algorithm as compared with other methods including conventional PCA (Principal Component Analysis)/ICA (Independent Component Analysis)/LDA methods and the previous MPEG-7 proposals.
15149884
Inverse halftoning by decision tree learning
Inverse halftoning is the process to retrieve a (gray) continuous-tone image from a halftone. Recently, machine-learning-based inverse halftoning techniques have been proposed. Decision-tree learning has been applied with success to various machine-learning applications for quite some time. In this paper, we propose to use decision-tree learning to solve the inverse halftoning problem. This allows us to reuse a number of algorithms already developed. Especially, the maximization of entropy gain is a powerful idea that makes the learning algorithm to automatically select the ideal window as the decision-tree is constructed. The new technique has generated gray images with PSNR numbers, which are several dB above those previously reported in the literature. Moreover, it possesses very fast implementation, lending itself useful for real time applications.
11702143
Adaptive nonlinear discriminant analysis by regularized minimum squared errors
Kernelized nonlinear extensions of Fisher's discriminant analysis, discriminant analysis based on generalized singular value decomposition (LDA/GSVD), and discriminant analysis based on the minimum squared error formulation (MSE) have recently been widely utilized for handling undersampled high-dimensional problems and nonlinearly separable data sets. As the data sets are modified from incorporating new data points and deleting obsolete data points, there is a need to develop efficient updating and downdating algorithms for these methods to avoid expensive recomputation of the solution from scratch. In this paper, an efficient algorithm for adaptive linear and nonlinear kernel discriminant analysis based on regularized MSE, called adaptive KDA/RMSE, is proposed. In adaptive KDA/RMSE, updating and downdating of the computationally expensive eigenvalue decomposition (EVD) or singular value decomposition (SVD) is approximated by updating and downdating of the QR decomposition achieving an order of magnitude speed up. This fast algorithm for adaptive kernelized discriminant analysis is designed by utilizing regularization techniques and the relationship between linear and nonlinear discriminant analysis and the MSE. In addition, an efficient algorithm to compute leave-one-out cross validation is also introduced by utilizing downdating of KDA/RMSE.
558514
Missing value estimation for DNA microarray gene expression data: local least squares imputation
MOTIVATION
Gene expression data often contain missing expression values. Effective missing value estimation methods are needed since many algorithms for gene expression data analysis require a complete matrix of gene array values. In this paper, imputation methods based on the least squares formulation are proposed to estimate missing values in the gene expression data, which exploit local similarity structures in the data as well as least squares optimization process.


RESULTS
The proposed local least squares imputation method (LLSimpute) represents a target gene that has missing values as a linear combination of similar genes. The similar genes are chosen by k-nearest neighbors or k coherent genes that have large absolute values of Pearson correlation coefficients. Non-parametric missing values estimation method of LLSimpute are designed by introducing an automatic k-value estimator. In our experiments, the proposed LLSimpute method shows competitive results when compared with other imputation methods for missing value estimation on various datasets and percentages of missing values in the data.


AVAILABILITY
The software is available at http://www.cs.umn.edu/~hskim/tools.html


CONTACT
hpark@cs.umn.edu