Wie ist die Zusammenarbeit in einem Forschungsverbund, an dem mehrere Disziplinen beteiligt sind, zu gestalten? Was ist zu beachten, wenn Personen aus der Praxis mitwirken? Wie sind gemeinsame Ziele und Fragen zu formulieren? Wie lässt sich die Vernetzung im Verbund fördern, wie kommt er zu einer Synthese? Wie kann das Engagement für das Gemeinsame erhalten werden? Das Handbuch liefert allen, die für die Planung und Durchführung von inter- und transdisziplinären Forschungsprojekten verantwortlich sind, handlungsorientierte Grundlagen. Es beschreibt die Anforderungen und Aufgaben des Forschungsverbundmanagements, bietet Management-verantwortlichen zahlreiche Tips und Beispiele und weist auf drohende Gefahren hin. Das Buch basiert auf einer empirischen Untersuchung von vier Forschungsprogrammen aus Deutschland, Österreich und der Schweiz (DACH-Erhebung). Es verbindet Ansätze aus verschiedenen Disziplinen und beruht auf den Erfahrungen der Autorin und der Autoren im Management von Forschungsverbünden, in der Beratung von Projekten und wissenschaftlichen Organisationen und in der Durchführung von Weiterbildungsveranstaltungen für Verantwortliche inter- und trans-disziplinärer Forschungsprojekte.

Reduced precision computation is a key enabling factor for energy-efficient acceleration of deep learning (DL) applications. This article presents a 7-nm four-core mixed-precision artificial intelligence (AI) chip that supports four compute precisions—FP16, Hybrid-FP8 (HFP8), INT4, and INT2—to support diverse application demands for training and inference. The chip leverages cutting-edge algorithmic advances to demonstrate leading-edge power efficiency for 8-bit floating-point (FP8) training and INT4 inference without model accuracy degradation. A new HFP8 format combined with separation of the floating- and fixed-point pipelines and aggressive circuit/architecture optimization enables performance improvements while maintaining high compute utilization. A high-bandwidth ring protocol enables efficient data communication, while power management using workload-aware clock throttling maximizes performance within a given power budget. The AI chip demonstrates 3.58-TFLOPS/W peak energy efficiency and 26.2-TFLOPS peak performance for HFP8 iso-accuracy training, and 16.9-TOPS/W peak energy efficiency and 104.9-TOPS peak performance for INT4 iso-accuracy inference.

3-D integration using through-silicon-vias (TSVs) is emerging as one of the key technology options for continued miniaturization. However, because of increased device and current density, the reliability of the 3-D power grid and its integrity must be studied and analyzed. Due to the geometry of TSVs and connections to the global power grid, significant current crowding can occur. Current densities at these connections can be much higher than the expected average values, so extra care is required for accurate analysis. In prior work, TSVs are modeled as single resistors along with power grid wire segments. Such models do not capture detailed current density distribution and may miss hotspots associated with current crowding. This paper studies current crowding and its impact on 3-D power grid integrity. First, we explore the current density distribution within a TSV and its connections to the global chip power grid. Second, we implement simple TSV models to obtain current density distributions within a TSV and its local environment. These models are checked for accuracy by comparing with models simulated using finite element modeling methods. Finally, the simple TSV models are integrated with the global power grid for detailed chip-scale power analysis.

BerichtIntelligente Steuerung der Forschung durch EvaluationHans Spada, Urs Baumann, Amelie Mummendey, Rolf Steyer, and Michael ScheuermannHans SpadaSearch for more papers by this author, Urs BaumannSearch for more papers by this author, Amelie MummendeySearch for more papers by this author, Rolf SteyerSearch for more papers by this author, and Michael ScheuermannSearch for more papers by this authorPublished Online:September 01, 2006https://doi.org/10.1026//0033-3042.54.4.246PDFView Full Text ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit SectionsMoreFiguresReferencesRelatedDetails Volume 54Issue 4Oktober 2003ISSN: 0033-3042eISSN: 2190-6238 InformationPsychologische Rundschau (2003), 54, pp. 246-248 https://doi.org/10.1026//0033-3042.54.4.246.© 2003Hogrefe-Verlag GöttingenPDF download

ReportsPsychological Research Supported by the European Science FoundationHans Spada, Peter Reimann, and Michael ScheuermannHans Spada University of Freiburg, Germany Search for more papers by this author, Peter Reimann University of Freiburg, Germany Search for more papers by this author, and Michael Scheuermann University of Freiburg, Germany Search for more papers by this authorPublished Online:November 10, 2006https://doi.org/10.1027/1016-9040.1.1.65PDFView Full Text ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit SectionsMoreFiguresReferencesRelatedDetailsCited byOntologies About Human Behavior A Review of Knowledge Modeling SystemsAngel Blanch, Roberto García, Jordi Planes, Rosa Gil, Ferran Balada, Eduardo Blanco, and Anton Aluja7 September 2017 | European Psychologist, Vol. 22, No. 3 Volume 1Issue 1March 1996ISSN: 1016-9040eISSN: 1878-531X InformationEuropean Psychologist (1996), 1, pp. 65-67 https://doi.org/10.1027/1016-9040.1.1.65.© 1996Hogrefe & Huber PublishersPDF download