4,656 research outputs found

    The future of computing beyond Moore's Law.

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    Moore's Law is a techno-economic model that has enabled the information technology industry to double the performance and functionality of digital electronics roughly every 2 years within a fixed cost, power and area. Advances in silicon lithography have enabled this exponential miniaturization of electronics, but, as transistors reach atomic scale and fabrication costs continue to rise, the classical technological driver that has underpinned Moore's Law for 50 years is failing and is anticipated to flatten by 2025. This article provides an updated view of what a post-exascale system will look like and the challenges ahead, based on our most recent understanding of technology roadmaps. It also discusses the tapering of historical improvements, and how it affects options available to continue scaling of successors to the first exascale machine. Lastly, this article covers the many different opportunities and strategies available to continue computing performance improvements in the absence of historical technology drivers. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'

    "Going back to our roots": second generation biocomputing

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    Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

    A Generalized Hybrid Real-Coded Quantum Evolutionary Algorithm Based on Particle Swarm Theory With Arithmetic Crossover

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    Digital Ecosystems: Ecosystem-Oriented Architectures

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    We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

    Ant Colony Optimization Based Subset Feature Selection in Speech Processing: Constructing Graphs with Degree Sequences

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    Feature selection or the process of selecting the most discriminating feature subset is an essential practice in speech processing that significantly affects the performance of classification. However, the volume of features that presents in speech processing makes the feature selection perplexing. Moreover, finding the optimal feature subset is a NP-hard problem (2n). Thus, a good searching strategy is required to avoid evaluating large number of combinations in the whole feature subsets. As a result, in recent years, many heuristic based search algorithms are developed to address this NP-hard problem. One of the several meta heuristic algorithms that is applied in many application domains to solve feature selection problem is Ant Colony Optimization (ACO) based algorithms.  ACO based algorithms are nature-inspired from the foraging behavior of actual ants. The success of an ACO based feature selection algorithm depends on the choice of the construction graph with respect to runtime behavior. While most ACO based feature selection algorithms use fully connected graphs, this paper proposes ACO based algorithm that uses graphs with prescribed degree sequences. In this method, the degree of the graph representing the search space will be predicted and the construction graph that satisfies the predicted degree will be generated. This research direction on graph representation for ACO algorithms may offer possibilities to reduce computation complexity from O(n2) to O(nm) in which m is the number of edges. This paper outlines some popular optimization based feature selection algorithms in the field of speech processing applications and overviewed ACO algorithm and its main variants. In addition to that, ACO based feature selection is explained and its application in various speech processing tasks is reviewed. Finally, a degree based graph construction for ACO algorithms is proposed

    Computational complexity of the landscape I

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    We study the computational complexity of the physical problem of finding vacua of string theory which agree with data, such as the cosmological constant, and show that such problems are typically NP hard. In particular, we prove that in the Bousso-Polchinski model, the problem is NP complete. We discuss the issues this raises and the possibility that, even if we were to find compelling evidence that some vacuum of string theory describes our universe, we might never be able to find that vacuum explicitly. In a companion paper, we apply this point of view to the question of how early cosmology might select a vacuum.Comment: JHEP3 Latex, 53 pp, 2 .eps figure
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