29502567
On silicon-based speed path identification
Speed path identification is an indispensable step for pushing the design timing wall and for developing the final speed binning strategy in production test. For complex high-performance designs, pre-silicon timing tools have so far not been able to deliver satisfactory results in predicting the actual speed limiting paths on the silicon. The actual speed paths are mostly uncovered through test and silicon debug, where tremendous manual effort is involved. This paper presents a novel approach as the first step for automating the speed path identification process. Our approach is silicon-based, meaning that timing information is extracted through testing of silicon sample chips. We call this step silicon learning. Based on silicon learning, we present an iterative flow for speed path identification. Experimental results are presented to explain the new methodologies and to demonstrate the effectiveness of our techniques.
52861235
On bounding the delay of a critical path
2032603
Similarity-Based Approaches to Natural Language Processing
Statistical methods for automatically extracting information about associations between words or documents from large collections of text have the potential to have considerable impact in a number of areas, such as information retrieval and natural-language-based user interfaces. However, even huge bodies of text yield highly unreliable estimates of the probability of relatively common events, and, in fact, perfectly reasonable events may not occur in the training data at all. This is known as the sparse data problem. Traditional approaches to the sparse data problem use crude approximations. We propose a different solution: if we are able to organize the data into classes of similar events, then, if information about an event is lacking, we can estimate its behavior from information about similar events. This thesis presents two such similarity-based approaches, where, in general, we measure similarity by the Kullback-Leibler divergence, an information-theoretic quantity. 
Our first approach is to build soft, hierarchical clusters: soft, because each event belongs to each cluster with some probability; hierarchical, because cluster centroids are iteratively split to model finer distinctions. Our clustering method, which uses the technique of deterministic annealing, represents (to our knowledge) the first application of soft clustering to problems in natural language processing. We use this method to cluster words drawn from 44 million words of Associated Press Newswire and 10 million words from Grolier's encyclopedia, and find that language models built from the clusters have substantial predictive power. Our algorithm also extends with no modification to other domains, such as document clustering. 
Our second approach is a nearest-neighbor approach: instead of calculating a centroid for each class, we in essence build a cluster around each word. We compare several such nearest-neighbor approaches on a word sense disambiguation task and find that as a whole, their performance is far superior to that of standard methods. In another set of experiments, we show that using estimation techniques based on the nearest-neighbor model enables us to achieve perplexity reductions of more than 20 percent over standard techniques in the prediction of low-frequency events, and statistically significant speech recognition error-rate reduction.
1473
Similarity-Based Models of Word Cooccurrence Probabilities
In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations “eat a peach” and ”eat a beach” is more likely. Statistical NLP methods determine the likelihood of a word combination from its frequency in a training corpus. However, the nature of language is such that many word combinations are infrequent and do not occur in any given corpus. In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on “most similar” words.We describe probabilistic word association models based on distributional word similarity, and apply them to two tasks, language modeling and pseudo-word disambiguation. In the language modeling task, a similarity-based model is used to improve probability estimates for unseen bigrams in a back-off language model. The similarity-based method yields a 20% perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speech-recognition error.We also compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency to avoid giving too much weight to easy-to-disambiguate high-frequency configurations. The similarity-based methods perform up to 40% better on this particular task.
11364382
The Generic Message Passing framework
The paper presents the design and implementation of the Generic Message Passing framework (GMP). The design of the GMP is guided by two goals: programmer productivity and run-time performance, particularly for parallel systems having both shared and distributed memory (e.g., clusters of SMPs). To achieve these goals simultaneously, the GMP uses a generic programming paradigm, allowing it to integrate seamlessly with modern generic libraries such as the C++ Standard Library. In addition the GMP presents a single parallel programming model, namely message passing. The implementation of the GMP fully exploits the architectural characteristics of its execution target for maximum run-time performance. Performance results are presented that demonstrate the effectiveness of the Generic Message Passing framework.
44673545
Generic programming for high-performance scientific applications
We present case studies that apply generic programming to the development of high-performance parallel code for solving two archetypal partial differential equations (PDEs). We examine the overall structure of the example scientific codes and consider their generic implementation. With a generic approach it is a straightforward matter to reuse software components from different sources; implementations with components from the Iterative Template Library (ITL), the Matrix Template Library (MTL), Blitz++, A++/P++, and Fortran BLAS are presented. Our newly developed Generic Message Passing library is used for communication. We compare the generic implementations with equivalent implementations developed with alternative libraries and languages and discuss performance as well as software engineering issues. Copyright © 2005 John Wiley & Sons, Ltd.
14581926
Fast Context-Free Parsing Requires Fast Boolean Matrix Multiplication
Valiant showed that Boolean matrix multiplication (BMM) can be used for CFG parsing. We prove a dual result: CFG parsers running in time O(|G||w|3-e) on a grammar G and a string w can be used to multiply m x m Boolean matrices in tmie O(m3-e/3). In the process we also provide a formal definition of parsing motivated by an informal notion due to Lang. Our result establishes one of the first limitations on general CFG parsing: a fast, practical CFG parser would yield a fast, practical BMM algorithm, which is not believed to exist.