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Titlebook: Applied Reconfigurable Computing. Architectures, Tools, and Applications; 18th International S Lin Gan,Yu Wang,Thomas Chau Conference proce

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樓主: Blandishment
31#
發(fā)表于 2025-3-26 23:48:49 | 只看該作者
Conference proceedings 2022 September 2022.. The 13 full papers presented in this volume were reviewed and selected from 16 submissions. The papers cover a broad spectrum of applications of reconfigurable computing, from driving assistance, data and graph processing acceleration, computer security to the societal relevant top
32#
發(fā)表于 2025-3-27 03:58:19 | 只看該作者
,100% Visibility at?MHz Speed: Efficient Soft Scan-Chain Insertion on?AMD/Xilinx FPGAs, to test an integrated circuit running at (near) speed with realistic inputs and outputs. The reconfigurable aspect of FPGA technology makes them suitable for hardware emulation and prototyping, plus their nature of having over-provisioned resources — inherently necessary to support the late-binding
33#
發(fā)表于 2025-3-27 08:39:12 | 只看該作者
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發(fā)表于 2025-3-27 09:38:44 | 只看該作者
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發(fā)表于 2025-3-27 17:05:41 | 只看該作者
,A Multi-FPGA Scalable Framework for?Deep Reinforcement Learning Through Neuroevolution, or training robots to perform human tasks. Training based on reinforcement implies the continuous interaction of the agent powered by the DNN and the environment, vanishing the typical separation between the training and inference stages in deep learning. However, the high memory and accuracy requi
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發(fā)表于 2025-3-27 17:51:00 | 只看該作者
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發(fā)表于 2025-3-27 22:38:56 | 只看該作者
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發(fā)表于 2025-3-28 02:57:59 | 只看該作者
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發(fā)表于 2025-3-28 09:18:38 | 只看該作者
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發(fā)表于 2025-3-28 13:10:00 | 只看該作者
,Hardware-Aware Optimizations for?Deep Learning Inference on?Edge Devices,ng of large amounts of data within a tight power budget. In this context, reconfigurable embedded devices make a compelling option. Deploying DL models to reconfigurable devices does, however, present considerable challenges. One key issue is reconciling the often large compute requirements of DL mo
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