5303914
An FPGA-Based Accelerator for Multiphysics Modeling
16276152
Hardware/Software Co-Design Architecture for Lattice Decoding Algorithms
This paper presents hardware/software co-design architecture targeted on a single FPGA for two typical lattice decoding algorithms in MIMO system. Two levels of parallelisms are analyzed for an efficient implementation with the preprocessing part on embedded MicroBlaze soft processor and the decoder part on customized hardware. The system prototypes of the AV and VB decoders show that they support up to 34.2 Mbps and 3.15 Mbps data rate respectively on XUP Virtex-II pro developing board, which are 19 and 16 times faster than their respective implementations on a DSP
7583168
Incremental Design Methodology for Multimillion-gate Fpgas
This paper presents an FPGA design methodology that can be used to shorten the FPGA design-and-debug cycle, especially as the gate counts increase to multimillions. Core-based incremental placement algorithms, in conjunction with fast interactive routing, are investigated to reduce the design processing time by distinguishing the changes between design iterations and reprocessing only the changed blocks without affecting the remaining part of the design. When combined with a background refinement thread, the incremental approach offers the instant gratification that designers expect, while preserving the fidelity attained through batch-oriented programs. An integrated FPGA design environment is then developed based on the incremental placer and its background refiner. The results show that the incremental design methodology is in orders of magnitude faster than the competing approaches such as the Xilinx M3 tools without sacrificing too much quality.
37047287
A SoPC Architecture of MIMO Sphere Decoder for Mobile Communications
15628775
An FPGA based Co-Design Architecture for MIMO Lattice Decoders
─ MIMO systems have attracted great attentions because of their huge capacity. The hardware implementation of MIMO decoder becomes a challenging task as the complexity of the MIMO systems increases. This paper presents hardware/software co-design architecture targeted on a single FPGA for two typical lattice decoding algorithms. Two levels of parallelisms are analyzed for an efficient implementation with the preprocessing part on embedded MicroBlaze soft processor and the decoder part on customized hardware. The system prototypes of the AV and VB decoders show that they support up to 34.2 Mbps and 3.15 Mbps data rate at 20dB SNR respectively on XUP Virtex-II pro developing board with an xc2vp30 FPGA, which are 19 and 16 times faster than their respective implementations on a DSP. The performance in terms of resource utilization and bit error rate are also compared between these two algorithms.
33920606
Hot-Spot Passage Retrieval in Question Answering
Question Answering has been the recent focus of information retrieval research; many systems just incorporate a search engine as a black box and most effort has been devoted to the question analyzer and the answer identifier. In the context of QA, however, passage provides an ideal medium between the document collection and an exact answer. And passage retrieval is a finer-grain approach than the traditional document retrieval both for the answer identifier and a human reader. In this paper, distinctions are first made between document retrieval and passage retrieval. And the Hot-Spot Passage Retrieval algorithm, which takes into account the measures of blurred BM25, coverage and height, is examined in detail. For evaluation, an isolated test is conducted and the algorithm gains 18.3% better answer redundancy and 4.8% better coverage rate than Okapi's original passage retrieval algorithm.
2661456
Linear FPT reductions and computational lower bounds
We develop new techniques for deriving very strong computational lower bounds for a class of well-known NP-hard problems, including weighted satisfiability, dominating set, hitting set, set cover, clique, and independent set. For example, although a trivial enumeration can easily test in time O(nk) if a given graph of n vertices has a clique of size k, we prove that unless an unlikely collapse occurs in parameterized complexity theory, the problem is not solvable in time f(k) no(k) for any function f, even if we restrict the parameter value k to be bounded by an arbitrarily small function of n. Under the same assumption, we prove that even if we restrict the parameter values k to be Θ(μ(n)) for any reasonable function μ, no algorithm of running time no(k) can test if a graph of n vertices has a clique of size k. Similar strong lower bounds are also derived for other problems in the above class. Our techniques can be extended to derive computational lower bounds on approximation algorithms for NP-hard optimization problems. For example, we prove that the NP-hard distinguishing substring selection problem, for which a polynomial time approximation scheme has been recently developed, has no polynomial time approximation schemes of running time f(1/ε)no(1/ε) for any function f unless an unlikely collapse occurs in parameterized complexity theory.
15247747
Clustering Web Surfers with Probabilistic Models in a Real Application
The problem of modeling and predicting a Web surfer's browsing patterns has gained increasing attention in recent years. In this paper we present our experience in clustering Web surfers using a mixture of Markov models with a real application of Livelink log data. We propose different techniques to improve the clustering performance, and evaluate the techniques through experiments.
945376
Text classification in Asian languages without word segmentation
We present a simple approach for Asian language text classification without word segmentation, based on statistical -gram language modeling. In particular, we examine Chinese and Japanese text classification. With character -gram models, our approach avoids word segmentation. However, unlike traditional ad hoc -gram models, the statistical language modeling based approach has strong information theoretic basis and avoids explicit feature selection procedure which potentially loses significantly amount of useful information. We systematically study the key factors in language modeling and their influence on classification. Experiments on Chinese TREC and Japanese NTCIR topic detection show that the simple approach can achieve better performance compared to traditional approaches while avoiding word segmentation, which demonstrates its superiority in Asian language text classification.
668000
Applying language modeling to session identification from database trace logs
A database session is a sequence of requests presented to the database system by a user or an application to achieve a certain task. Session identification is an important step in discovering useful patterns from database trace logs. The discovered patterns can be used to improve the performance of database systems by prefetching predicted queries, rewriting the current query or conducting effective cache replacement.In this paper, we present an application of a new session identification method based on statistical language modeling to database trace logs. Several problems of the language modeling based method are revealed in the application, which include how to select values for the parameters of the language model, how to evaluate the accuracy of the session identification result and how to learn a language model without well-labeled training data. All of these issues are important in the successful application of the language modeling based method for session identification. We propose solutions to these open issues. In particular, new methods for determining an entropy threshold and the order of the language model are proposed. New performance measures are presented to better evaluate the accuracy of the identified sessions. Furthermore, three types of learning methods, namely, learning from labeled data, learning from semi-labeled data and learning from unlabeled data, are introduced to learn language models from different types of training data. Finally, we report experimental results that show the effectiveness of the language model based method for identifying sessions from the trace logs of an OLTP database application and the TPC-C Benchmark.