35337480
On generating tests to cover diverse worst-case timing corners
With process variations, timing behavior may vary from chip to chip. This paper investigates the problem of generating test patterns to cover potentially diverse worst-case timing corners. We focus the work on a specific problem formulation where the delay of a path can be affected by k aggressors. We demonstrate that the search space for such a problem can be quite complex. We study various methods to guide the test generation. We show that with different chips having different worst-case corners, it may not be affordable to search for the tests to expose all these corners. Experimental results are presented to explain the problem formulation, the test generation methods, and the limitation on what we can achieve for solving the problem.
52861235
On bounding the delay of a critical path
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.
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.
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.
312731
Mostly-Unsupervised Statistical Segmentation of Japanese: Applications to Kanji
Given the lack of word delimiters in written Japanese, word segmentation is generally considered a crucial first step in processing Japanese texts. Typical Japanese segmentation algorithms rely either on a lexicon and grammar or on pre-segmented data. In contrast, we introduce a novel statistical method utilizing unsegmented training data, with performance on kanji sequences comparable to and sometimes surpassing that of morphological analyzers over a variety of error metrics.
2480472
Similarity-Based Methods for Word Sense Disambiguation
We 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. The similarity-based methods perform up to 40% better on this particular task. We also conclude that events that occur only once in the training set have major impact on similarity-based estimates.
1243491
Fast context-free grammar parsing requires fast boolean matrix multiplication
In 1975, Valiant showed that Boolean matrix multiplication can be used for parsing context-free grammars (CFGs), yielding the asympotically fastest (although not practical) CFG parsing algorithm known. We prove a dual result: any CFG parser with time complexity <i>O</i>(<i>gn</i><sup>3-∈</sup>), where <i>g</i> is the size of the grammar and <i>n</i> is the length of the input string, can be efficiently converted into an algorithm to multiply <i>m</i> × <i>m</i> Boolean matrices in time <i>O</i>(<i>m</i><sup>3-∈/3</sup>). Given that practical, substantially subcubic Boolean matrix multiplication algorithms have been quite difficult to find, we thus explain why there has been little progress in developing practical, substantially subcubic general CFG parsers. In proving this result, we also develop a formalization of the notion of parsing.