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

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Applied Reconfigurable Computing. Architectures, Tools, and Applications978-3-319-78890-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

大洪水

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.

defeatist

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

Psa617

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

擦掉

CAMP

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

Talkative

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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