03463oam 2200493 450 991029974520332120190911103512.01-4614-7798-010.1007/978-1-4614-7798-3(OCoLC)858403117(MiFhGG)GVRL6USJ(EXLCZ)99267000000042748920140415d2014 uy 0engurun|---uuuuatxtccrComputing with memory for energy-efficient robust systems /Somnath Paul, Swarup Bhunia1st ed. 2014.New York :Springer,2014.1 online resource (xiii, 210 pages) illustrations (some color)Gale eBooksDescription based upon print version of record.1-4614-7797-2 Includes bibliographical references.Part I Introduction -- Challenges in Computing for Nanoscale Technologies -- A Survey of Computing Architectures -- Motivation for a Memory-Based Computing Hardware -- Part II Memory Based Computing -- Key Features of Memory-Based Computing -- Overview of Hardware and Software Architectures -- Application of Memory-Based Computing -- Part III Hardware Framework -- A Memory Based Generic Reconfigurable Framework -- MAHA Hardware Architecture -- Part IV Software Framework -- Application Analysis -- Application Mapping to MBC Hardware.This book analyzes energy and reliability as major challenges faced by designers of computing frameworks in the nanometer technology regime.  The authors describe the existing solutions to address these challenges and then reveal a new reconfigurable computing platform, which leverages high-density nanoscale memory for both data storage and computation to maximize the energy-efficiency and reliability. The energy and reliability benefits of this new paradigm are illustrated and the design challenges are discussed. Various hardware and software aspects of this exciting computing paradigm are described, particularly with respect to hardware-software co-designed frameworks, where the hardware unit can be reconfigured to mimic diverse application behavior.  Finally, the energy-efficiency of the paradigm described is compared with other, well-known reconfigurable computing platforms.  ·         Introduces new paradigm for hardware reconfigurable frameworks, which leverages dense memory array as a malleable resource, which can be used for information storage as well as computation; ·         Merges spatial and temporal computing to minimize interconnect overhead and achieve better scalability compared to state-of-the-art reconfigurable computing platforms; ·         Enables efficient mapping of diverse data-intensive applications from domains of signal processing, multimedia and security applications.Nanoelectromechanical systemsComputer engineeringNanoelectromechanical systems.Computer engineering.004.1620621.381621.3815Paul Somnathauthttp://id.loc.gov/vocabulary/relators/aut957747Bhunia SwarupMiFhGGMiFhGGBOOK9910299745203321Computing with Memory for Energy-Efficient Robust Systems2169653UNINA06305nam 22008415 450 991048362100332120251226195057.03-642-16184-710.1007/978-3-642-16184-1(CKB)2670000000056639(SSID)ssj0000446424(PQKBManifestationID)11327272(PQKBTitleCode)TC0000446424(PQKBWorkID)10496098(PQKB)10057121(DE-He213)978-3-642-16184-1(MiAaPQ)EBC3066075(PPN)149890699(BIP)32335734(EXLCZ)99267000000005663920101102d2010 u| 0engurnn|008mamaatxtccrDiscovery Science 13th International Conference, DS 2010, Canberra, Australia, October 6-8, 2010, Proceedings /edited by Bernahrd Pfahringer, Geoff Holmes, Achim Hoffman1st ed. 2010.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2010.1 online resource (XIII, 384 p. 108 illus.) Lecture Notes in Artificial Intelligence,2945-9141 ;6332Bibliographic Level Mode of Issuance: Monograph3-642-16183-9 Includes bibliographical references and index.Sentiment Knowledge Discovery in Twitter Streaming Data -- A Similarity-Based Adaptation of Naive Bayes for Label Ranking: Application to the Metalearning Problem of Algorithm Recommendation -- Topology Preserving SOM with Transductive Confidence Machine -- An Artificial Experimenter for Enzymatic Response Characterisation -- Subgroup Discovery for Election Analysis: A Case Study in Descriptive Data Mining -- On Enumerating Frequent Closed Patterns with Key in Multi-relational Data -- Why Text Segment Classification Based on Part of Speech Feature Selection -- Speeding Up and Boosting Diverse Density Learning -- Incremental Learning of Cellular Automata for Parallel Recognition of Formal Languages -- Sparse Substring Pattern Set Discovery Using Linear Programming Boosting -- Discovery of Super-Mediators of Information Diffusion in Social Networks -- Integer Linear Programming Models for Constrained Clustering -- Efficient Visualization of Document Streams -- Bridging Conjunctive and Disjunctive Search Spaces for Mining a New Concise and Exact Representation of Correlated Patterns -- Graph Classification Based on Optimizing Graph Spectra -- Algorithm for Detecting Significant Locations from Raw GPS Data -- Discovery of Conservation Laws via Matrix Search -- Gaussian Clusters and Noise: An Approach Based on the Minimum Description Length Principle -- Exploiting Code Redundancies in ECOC -- Concept Convergence in Empirical Domains -- Equation Discovery for Model Identification in Respiratory Mechanics of the Mechanically Ventilated Human Lung -- Mining Class-Correlated Patterns for Sequence Labeling -- ESTATE: Strategy for Exploring Labeled Spatial Datasets Using Association Analysis -- Adapted Transfer of Distance Measures for Quantitative Structure-Activity Relationships -- Incremental Mining of Closed Frequent Subtrees -- Optimal Online Prediction in Adversarial Environments -- Discovery of Abstract Concepts by a Robot -- Contrast Pattern Mining and Its Application for Building Robust Classifiers -- Towards General Algorithms for Grammatical Inference -- The Blessing and the Curse of the Multiplicative Updates.The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R & D community, with numerous individuals, as well as with prestigious organizations and societies, LNAI has grown into the most comprehensive artificial intelligence research forum available. The scope of LNAI spans the whole range of artificial intelligence and intelligent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes proceedings (published in time for the respective conference) post-proceedings (consisting of thoroughly revised final full papers) research monographs (which may be based on PhD work) More recently, several color-cover sublines have been added featuring, beyond a collection of papers, various added-value components; these sublines include tutorials (textbook-like monographs or collections of lectures given at advance courses) state-of-the-art surveys (offering complete and mediated coverage of a topic) hot topics (introducing emergent topics to the broader community) In parallel to the printed book, each new volume is published electronically in LNCS Online. Book jacket.Lecture Notes in Artificial Intelligence,2945-9141 ;6332Artificial intelligenceInformation storage and retrieval systemsApplication softwareDatabase managementData miningAlgorithmsArtificial IntelligenceInformation Storage and RetrievalComputer and Information Systems ApplicationsDatabase ManagementData Mining and Knowledge DiscoveryAlgorithmsArtificial intelligence.Information storage and retrieval systems.Application software.Database management.Data mining.Algorithms.Artificial Intelligence.Information Storage and Retrieval.Computer and Information Systems Applications.Database Management.Data Mining and Knowledge Discovery.Algorithms.501Pfahringer Bernhard1758420Holmes Geoffrey615305Hoffmann Achim1618538International Conference on Discovery ScienceMiAaPQMiAaPQMiAaPQBOOK9910483621003321Discovery science4198488UNINA