Titlebook: Applied Reconfigurable Computing. Architectures, Tools, and Applications; 14th International S Nikolaos Voros,Michael Huebner,Pedro C. Dini

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Image Re-processing of Satellite Imageries,searchers have found that the achievable performance highly depends on the interface between memory and CGRA. In this contribution we show that a smart prefetching mechanism can increase the performance of the CGRA. At the same time it consumes less hardware resources and energy as state of the art

amputation

Surface Water Pathway Analysis, mapping large designs is greatly affected by the long runtime of FPGA CAD flow. To mitigate it, modular design methodology has been introduced in the past that allows designers to partition large designs into smaller modules and compile & test the modules individually before assembling them togethe

Immunotherapy

Principles of Environmental Modeling,hitecture platform. KF is realized using Modified Faddeeva Algorithm (MFA) as a basic building block due to its versatility and REDEFINE Coarse Grained Reconfigurable Architecture (CGRA) is used as a platform for experiments since REDEFINE is capable of supporting realization of a set algorithmic co

Statins

Nuno Videira,Paula Antunes,Rui Santosng or pattern recognition in artificial intelligence while realizing high cost-performance and energy efficiency. To achieve this goal, optimizing different aspects of the system communication is a key challenge. In order to do this, a preliminary study on the application mapping for both the execut

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Environmental Modelling and Predictionin using them for further computations. In this paper, we present an FPGA accelerator for checking resolution proofs, a popular proof format. Our accelerator exploits parallelism at the low level by implementing the basic resolution step in hardware, and at the high level by instantiating a number o

灯泡

https://doi.org/10.1007/978-3-662-04868-9rmed in two phases: in the Bing Bang phase, similarly to other Genetic Algorithms (GAs) it generates a random population of candidate solutions, while in the Big Crunch phase it shrinks these candidates around an optimal point via a center-of-mass or minimal cost approach. It has been shown that the

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思乡病

Ocean Modelling and Prediction,-speed networks, NIDS needs to be high-speed hardware and parallelization is inevitable. The pattern matching of PCRE (Perl Compatible Regular Expressions) is one of the most complex parts in NIDS. We tried to improve the parallelization of PCRE pattern matching in Snort, implementing it on an FPGA.

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https://doi.org/10.1007/978-3-319-78890-6artificial intelligence; computer architecture; data communication systems; energy efficiency; field pro

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978-3-319-78889-0Springer International Publishing AG, part of Springer Nature 2018