7360354
Fast performance-driven optimization for buffered clock trees based on Lagrangian relaxation
Delay, power, skew, area, and sensitivity are the most important concerns in current clock-tree design. We present in this paper an algorithm for simultaneously optimizing the above objectives by sizing wires and buffers in clock trees. Our algorithm, based on Lagrangian relaxation method, can optimally minimize delay, power, and area simultaneously with very low skew and sensitivity. With linear storage overall and linear runtime per iteration, our algorithm is extremely economical, fast, and accurate; for example, our algorithm can solve a 6201-wire-segment clock-free problem using about 1-minute runtime and 1.3-MB memory and still achieve pico-second precision on an IBM RS/6000 workstation.
12098402
Noise-aware repeater insertion and wire-sizing for on-chip interconnect using hierarchical moment-matching
Recently, several algorithms for interconnect optimization via repeater insertion and wire sizing have appeared based on the Elmore delay model. Using the Devgan noise metric a noise-aware repeater insertion technique has also been proposed recently. Recognizing the conservatism of these delay and noise models, we propose a moment-matching based technique to interconnect optimization that allows for much higher accuracy while preserving the hierarchical nature of Elmore-delay-based techniques. We also present a novel approach to noise computation that accurately captures the effect of several attackers in linear time with respect to the number of attackers and wire segments. Our practical experiments with industrial nets indicate that the corresponding reduction in error afforded by these more accurate models justifies this increase in runtime for aggressive designs which is our targeted domain. Our algorithm yields delay and noise estimates within 5% of circuit simulation results.
13521805
A Systematic Approach to Model-Guided Empirical Search for Memory Hierarchy Optimization
The goal of this work is a systematic approach to compiler optimization for simultaneously optimizing across multiple levels of the memory hierarchy. Our approach combines compiler models and heuristics with guided empirical search to take advantage of their complementary strengths. The models and heuristics limit the search to a small number of candidate implementations, and the empirical results provide accurate feedback information to the compiler. In previous work, we propose a compiler algorithm for deriving a set of parameterized solutions, followed by a model-guided empirical search to determine the best integer parameter values and select the best overall solution. This paper focuses on formalizing the process of deriving parameter values, which is a multi-variable optimization problem, and considers the role of AI search techniques in deriving a systematic framework for the search.
14219803
A new method to measure the semantic similarity of GO terms
MOTIVATION
Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources.


RESULTS
To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery.


AVAILABILITY
The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME.


SUPPLEMENTARY INFORMATION
http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.
15331077
Precoded V-BLAST for ISI MIMO channels
The V-BLAST (vertical Bell labs layered space-time) system is one of the MIMO systems designed to achieve a good multiplexing gain. In the recent literature, a V-BLAST precoder has been added in the transmitter which exploits channel information. This precoder forces each symbol stream to have identical MSE (mean square error). It can be viewed as an alternative to the bitloading method. In this paper, this precoded V-BLAST is extended to the case of ISI MIMO channels. Both the FIR and OFDM types of transceivers are derived
45162382
Efficient priority-first search maximum-likelihood soft-decision decoding of linear block codes
The authors present a novel and efficient maximum-likelihood soft-decision decoding algorithm for linear block codes. The approach used here converts the decoding problem into a search problem through a graph that is a trellis for an equivalent code of the transmitted code. A generalized Dijkstra's algorithm, which uses a priority-first search strategy, is employed to search through this graph. This search is guided by an evaluation function f defined to take advantage of the information provided by the received vector and the inherent properties of the transmitted code. This function f is used to reduce drastically the search space and to make the decoding efforts of this decoding algorithm adaptable to the noise level. For example, for most real channels of the 35 000 samples tried, simulation results for the (128,64) binary extended BCH code show that the proposed decoding algorithm is fifteen orders of magnitude more efficient in time and in space than that proposed by Wolf (1978). Simulation results for the (104, 52) binary extended quadratic residue code are also given. >
6983146
On Digital Mammogram Segmentation and Microcalcification Detection Using Multiresolution Wavelet Analysis
In this paper a multiresolution wavelet analysis (MWA) and nonstationary Gaussian Markov random field (GMRF) technique is introduced for the detection of microcalcifications with high accuracy. The hierarchical multiresolution wavelet information in conjunction with the contextual information of the images extracted from GMRF provides an efficient technique for microcalcification detection. A Bayesian learning paradigm realized via the expectation maximization (EM) algorithm was also introduced for edge detection or segmentation of mass regions recorded on the mammograms. The strength of the technique is in the effective utilization of the rich contextural information in the images considered. The effectiveness of the approach has been tested with a number of mammographic images for which the microcalcification detection algorithm achieved a sensitivity (true positive rate) of 94% and specificity (true negative rate) of 88%. Considerably good results were also obtained for the segmentation algorithm. In addition, the results for both the detected microcalcifications and the segmented mass regions were superimposed for an interesting case under the methods introduced.