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

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發(fā)表于 2025-3-21 16:06:51 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Applied Reconfigurable Computing. Architectures, Tools, and Applications
期刊簡(jiǎn)稱14th International S
影響因子2023Nikolaos Voros,Michael Huebner,Pedro C. Diniz
視頻videohttp://file.papertrans.cn/161/160099/160099.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Applied Reconfigurable Computing. Architectures, Tools, and Applications; 14th International S Nikolaos Voros,Michael Huebner,Pedro C. Dini
影響因子This book constitutes the proceedings of the 14th International Conference on Applied Reconfigurable Computing, ARC 2018, held in Santorini, Greece, in May 2018.?. The 29 full papers and 22 short presented in this volume were carefully reviewed and selected from 78 submissions. In addition, the volume contains 9 contributions from research projects. The papers were organized in topical sections named: machine learning and neural networks; FPGA-based design and CGRA optimizations; applications and surveys; fault-tolerance, security and communication architectures; reconfigurable and adaptive architectures; design methods and fast prototyping; FPGA-based design and applications; and special session: research projects.?.
Pindex Conference proceedings 2018
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Applied Reconfigurable Computing. Architectures, Tools, and Applications978-3-319-78890-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
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發(fā)表于 2025-3-22 04:28:47 | 只看該作者
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 prefetching mechanisms.
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Stacia Ryder,Michael Mikulewiczing Artificial Intelligence tasks. Nevertheless, the highest performing LSTM models are becoming increasingly demanding in terms of computational and memory load. At the same time, emerging latency-sensitive applications including mobile robots and autonomous vehicles often operate under stringent c
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Potential and Flow Visualizationred to many conventional feature-based computer vision algorithms. However, the high computational complexity of CNN models can lead to low system performance in power-efficient applications. In this work, we firstly highlight two levels of model redundancy which widely exist in modern CNNs. Additio
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https://doi.org/10.1007/978-3-642-22042-5nected to other nodes. Unlike other multi-FPGA systems, the circuit switching fabric with the STDM (Static Time Division Multiplexing) is implemented on the FPGA for predictable communication and cost-efficient data broadcasting. Parallel convolution modules for AlexNet are implemented on FiC-SW1 pr
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Streamfunction and Complex Potentialsuch as object recognition and object detection. Most of these solutions come at a huge computational cost, requiring billions of multiply-accumulate operations and, thus, making their use quite challenging in real-time applications that run on embedded mobile (resource-power constrained) hardware.
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