With attractive advantages like high density and low leakage, Spin-Transfer Torque Magnetoresistive RAM (STT-MRAM) is a promising candidate to replace conventional SRAM technology to build large-size and low-power on-...
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ISBN:
(纸本)9781479939466
With attractive advantages like high density and low leakage, Spin-Transfer Torque Magnetoresistive RAM (STT-MRAM) is a promising candidate to replace conventional SRAM technology to build large-size and low-power on-chip caches. Multi-level cell (MLC) STT-MRAM, with a higher density, further improves the on-chip cache capacity for chip multiprocessor (CMP) systems. However, the notorious high write energy impedes the adoption of MLC STT-MRAM. In this paper, we focus on minimizing the energy consumption during MLC STT-MRAM write operations. Based on the strong dependency of write energy on data values, a dynamic encoding technique is proposed to map the most frequently appearing data patterns to the most energy-efficient resistance states at runtime. Our experimental results show that, compared with the existing static data mapping scheme, our technique reduces write energy by 12.4% on average and up to 25.4% for a typical MLC STT-MRAM cache.
We present OASSIS (for Ontology ASSISted crowd mining), a prototype system which allows users to declaratively specify their information needs, and mines the crowd for answers. The answers that the system computes are...
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We present OASSIS (for Ontology ASSISted crowd mining), a prototype system which allows users to declaratively specify their information needs, and mines the crowd for answers. The answers that the system computes are concise and relevant, and represent frequent, significant data patterns. The system is based on (1) a generic model that captures both ontological knowledge, as well as the individual knowledge of crowd members from which frequent patterns are mined;(2) a query language in which users can specify their information needs and types of data patterns they seek;and (3) an efficient query evaluation algorithm, for mining semantically concise answers while minimizing the number of questions posed to the crowd. We will demonstrate OASSIS using a couple of real-life scenarios, showing how users can formulate and execute queries through the OASSIS UI and how the relevant data is mined from the crowd.
In bit patterned magnetic recording (BPMR), The two-dimensional (2D) interference composed of inter-symbol and inter-track interference is a major problem especially at high areal density (AD). One way to alleviate th...
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ISBN:
(纸本)9786163618238
In bit patterned magnetic recording (BPMR), The two-dimensional (2D) interference composed of inter-symbol and inter-track interference is a major problem especially at high areal density (AD). One way to alleviate the destructive effect of 2D interference is to deploy a 2D coding scheme on an input data sequence before recording in order to avoid some data patterns that easily cause an error at the data readback process. However, some coding schemes generate high complexity. Consequently, this paper proposes a new low-complexity modulation code with redundant bits to eliminate the data patterns leading to severe 2D interference and compare overall system performance. Experimental results indicate that the system with the proposed code is superior to that without coding, especially when the AD is high and/or the position jitter is large.
The examination timetabling problem (ETP) is a NP complete, combinatorial optimization problem. Intuitively, use of properties such as patterns or clusters in the data suggests possible improvements in the performance...
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The examination timetabling problem (ETP) is a NP complete, combinatorial optimization problem. Intuitively, use of properties such as patterns or clusters in the data suggests possible improvements in the performance and quality of timetabling. This paper investigates whether the use of a genetic algorithm (GA) informed by patterns extracted from student timetable data to solve ETPs can produce better quality solutions. The data patterns were captured in clusters, which then were used to generate the initial population and evaluate fitness of individuals. The proposed techniques were compared with a traditional GA and popular techniques on widely used benchmark problems, and a local data set, the Australian National University (ANU) ETP, which was the motivating problem for this work. A formal definition of the ANU ETP is also proposed. Results show techniques using cluster patterns produced better results than the traditional GA with statistical significance of p < 0.01, showing strong evidence. Our techniques either clearly outperformed or performed well compared to the best known techniques in the literature and produced a better timetable than the manually constructed timetable used by ANU, both in terms of quality and execution time. In this work, we also propose clear criteria for specifying the top results in this area.
In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different rel...
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In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.
An augmentation of a time-division switch is proposed so that this switch has an additional property of fault self-detection. data patterns from normal operation (i.e. speech samples and control signals) serve as test...
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An augmentation of a time-division switch is proposed so that this switch has an additional property of fault self-detection. data patterns from normal operation (i.e. speech samples and control signals) serve as test patterns and a failure occurrence caused by some intermittent or solid fault can be detected by means of a built-in testing circuit
In the past, methods for rejection of outliers have been investigated mostly without regard to the quantitative consequences for subsequent estimation or testing procedures. Moreover, although rejection of outliers wi...
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In the past, methods for rejection of outliers have been investigated mostly without regard to the quantitative consequences for subsequent estimation or testing procedures. Moreover, although rejection of outliers with subsequent application of least squares methods is one of the oldest and most widespread classes of robust procedures, until recently no comparison was made with other robust methods. In this article the simplest situation, namely estimation of a location parameter in the potential presence of outliers, is treated by means of a Monte Carlo study. This study yields Monte Carlo variances of the "arithmetic mean" after rejection of outliers according to several classical and recent formal rules. The results are also compared with those for other robust estimators of location parameters. It turns out that a simple summary and theoretical explanation of the Monte Carlo results is provided by the breakdown points of the combined rejection-estimation procedures. As a by-product, the concept of breakdown point also leads to a better understanding of the so-called "masking effect" and can in fact replace the latter concept. Formulas for the breakdown points are given for the six types of rejection rules used. Some general aspects and properties of all methods for the rejection of outliers and their relation to other robust methods are also discussed. Finally, the treatment of outliers in the context of real data is considered, and several examples are briefly mentioned; one real-life example is analyzed in greater detail.
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