this paper introduces PAPA: Packed Arithmetic on a Prefix Adder, a new approach to parallel prefix adder design that supports a wide variety of packed arithmetic computations, including packed add and subtract with sa...
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ISBN:
(纸本)0769517129
this paper introduces PAPA: Packed Arithmetic on a Prefix Adder, a new approach to parallel prefix adder design that supports a wide variety of packed arithmetic computations, including packed add and subtract with saturation, packed rounded average, and packed absolute difference the approach consists of altering the prefix adder cell logic equations to take advantage of a previously unused "don't care " state. Logical Effort is employed to assess the delay of the new adder architecture by establishing the extra effort needed to select and drive the appropriate carry signal to the requisite sum sub-word. this adder will find applications in video processors and other multimedia-orientated processor chips that implement packed arithmetic operations.
this paper presents tensor product formulas for modeling fault tolerant architectures and their corresponding reconfiguration algorithms. In our approaches, a network topology is first described with simple tensor pro...
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In most distributed memory computations, node programs are executed on processors according to the owner computes rule. However, the owner computes rule is not best suited for irregular application codes. In irregular...
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A consensus on a parallel architecture for very large database management has emerged. this architecture is based on a shared-nothing hardware organization. the computation model is very sensitive to skew in tuple dis...
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the proceedings contain 101 papers. the special focus in this conference is on Grid architectures, Load Balancing, Performance Analysis, Prediction, parallel Non-numerical algorithms and parallel Programming. the topi...
ISBN:
(纸本)9783540437925
the proceedings contain 101 papers. the special focus in this conference is on Grid architectures, Load Balancing, Performance Analysis, Prediction, parallel Non-numerical algorithms and parallel Programming. the topics include: Interrupt and cancellation as synchronization methods;applications of virtual data in the LIGO experiment;a parallel system architecture based on dynamically configurable shared memory clusters;simultaneous allocation and scheduling with exclusion and precedence relations algorithm;a greedy approach for a time-dependent scheduling problem;dedicated scheduling of biprocessor tasks to minimize mean flow time;heterogeneous dynamic load balancing with a scheme based on the laplacian polynomial;task scheduling for dynamically configurable multiple SMP clusters based on extended DSC approach;processing time and memory requirements for multi-instalment divisible job processing;estimating execution time of distributed applications;evaluation of parallel programs by measurement of its granularity;the performance of different communication mechanisms and algorithms used for parallelization of molecular dynamics code;benchmarking tertiary storage systems with file fragmentation;fem computations on clusters using different models of parallel programming;parallel skeletons for tabu search method based on search strategies and neighborhood partition;a new parallel approach for multi-dimensional packing problems;three parallelalgorithms for simulated annealing;solving the flow shop problem by parallel simulated annealing;automated verification of infinite state concurrent systems;criteria of satisfiability for homogeneous systems of linear Diophantine constraints and irregular and out-of-core parallel computing on clusters.
We propose a parallel sub-word recognition system (PSWR) as an alternative to the parallel phone recognition (PPR) system conventionally reported for language identification (LID) task. the sub-word recognizer (SWR) u...
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We propose a parallel sub-word recognition system (PSWR) as an alternative to the parallel phone recognition (PPR) system conventionally reported for language identification (LID) task. the sub-word recognizer (SWR) used in the PSWR system can be obtained from training data without phonetic transcription in any of the languages in the task. It is based on automatic segmentation followed by segment clustering and segment HMM modeling. the SWR can replace the front-end phone recognizer (PR) in the PPR system as well as in the PRLM and P-PRLM systems which constitute two other well accepted frameworks in LID system design. this allows easy expansion of these systems to a large number of languages without requiring tedious manually labeled training speech data in any of the languages in the task. On a 6 language LID task, using the OGI-TS database, we show that the PSWR system performs comparably to the PPR system, thus providing an efficient automatic alternative.
the network computing industry has eagerly embraced technologies, welcoming an ever-increasing variety of new service discovery protocols and object architectures. Withthis abundance now offered across a wide collect...
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ISBN:
(纸本)140207008X
the network computing industry has eagerly embraced technologies, welcoming an ever-increasing variety of new service discovery protocols and object architectures. Withthis abundance now offered across a wide collection of environments, technologies that offer standardized interfaces for the discovery process, while supporting communication for several different types of service access technologies, will provide the greatest achievable interoperability and resilience in the long-term. In this paper, we introduce a distributed architecture based on using directory services to significantly reduce the complexity of managing the information and services required to support next-generation networked applications, by providing automatic service discovery and a single coherent model for representing the data managed by supporting services. Standards-based solutions are used, and a prototype implementation of the CORBA Naming Service that has been designed to illustrate how the architecture incorporates distributed object models, directory services and multicast-based dynamic service discovery is presented.
Reducing mismatch between HMMs trained with clean speech and speech signals corrupted with background noise can be approached by speech distribution adaptation using parallel model combination (PMC). Accurate PMC has ...
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Reducing mismatch between HMMs trained with clean speech and speech signals corrupted with background noise can be approached by speech distribution adaptation using parallel model combination (PMC). Accurate PMC has no closed-form expression, therefore simplification assumptions must be made in implementation. Under three assumptions, i.e. log-normal, log-add and logmax, adaptation formula for log-spectral parameters are presented, both for static and dynamic parameters. Experimental evaluation uses TI-DIGITS speech database corrupted with car noise at 0dB signal-to-noise ratio. the recognition performance of the above three types of simplification is established. It is shown that, the adaptation of both static and dynamic parameters gives as much as 30% lower WER compared to adapting only static parameters. the findings and results presented in the paper provide a basis for trading-offs between recognition accuracy and computation requirement.
this paper proposes a new technique for adapting Hidden Markov Model (HMM) speech recognition systems to additive environmental noise by incorporating information about the uncertainty of observations. Current techniq...
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this paper proposes a new technique for adapting Hidden Markov Model (HMM) speech recognition systems to additive environmental noise by incorporating information about the uncertainty of observations. Current techniques, such as the parallel Model Combination (PMC) algorithm [1], are successful in steady state noise environments. However, the computational requirements both in processing time and memory prevent its widespread use in continuously adaptive systems, necessary for environments with changing background noise. this paper presents an approach that reformulates the model combination technique to update the each observation instead of the model. As in PMC, a model of the background is generated during nonspeech times. this is used for each input frame to generate a pdf describing the original clean signal, given the noise model. the HMM decoding algorithm is extended as in [2, 3] to allow pdf inputs, and recognition results are presented that show this technique compares favorably with PMC in unchanging noise environments, but has significant benefits in changing noise.
We propose methods to exploit contemporary news articles in a resource rich language, together with cross-language information retrieval and machine translation, to sharpen language models for a news story in a langua...
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We propose methods to exploit contemporary news articles in a resource rich language, together with cross-language information retrieval and machine translation, to sharpen language models for a news story in a language with fewer linguistic resources. We report experimental results on storyspecific Chinese language models that use cues from a parallel corpus of English news stories. We demonstrate that even with fairly crude cross-language information retrieval, level-1 machine translation and simple linear interpolation, a significant (18%) reduction in perplexity may be obtained over a Chinese trigram model. We also demonstrate that this method of sharpening the Chinese language model is complementary to other techniques like topic dependent modeling, and the two in combination result in an even greater reduction in perplexity (28%).
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