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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
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發(fā)表于 2025-3-23 12:04:54 | 只看該作者
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發(fā)表于 2025-3-23 16:41:03 | 只看該作者
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發(fā)表于 2025-3-23 18:15:24 | 只看該作者
,VenOS: A Virtualization Framework for?Multiple Tenant Accommodation on?Reconfigurable Platforms,ources among tenants, virtualizes memory and I/Os operations and offers strong data isolation against malicious transactions. Finally, VenOS comprises a resource manager based on memory segmentation, along with isolation modules that offer a protection layer between the accelerators and the system.
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發(fā)表于 2025-3-23 23:05:24 | 只看該作者
Kenneth S. Ramos,Abeer A. I. Hassaninwith SWLBM will be introduced to show the advantage of this software over other CFD code in large-scale simulations. SWLBM is still under development, with the continuous improvement of functions, it will play a greater role in the field of fluid simulation.
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發(fā)表于 2025-3-24 04:19:13 | 只看該作者
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發(fā)表于 2025-3-24 06:50:22 | 只看該作者
Joan H. Schiller,Jasmine Kambojed adaptive histogram equalization algorithm, Hessian matrix construction and region of interest function on the AMD-Xilinx’s Kria KV260 board. It outlines optimizations undertaken to reduce the BRAMs by 38%, DSP48 blocks by 80%, flip-flops by 33% and LUTs by 36%, thus creating a design operating at 121 FPS.
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發(fā)表于 2025-3-24 10:57:22 | 只看該作者
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發(fā)表于 2025-3-24 16:20:58 | 只看該作者
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發(fā)表于 2025-3-24 21:28:47 | 只看該作者
Multi-spectral In-Vivo FPGA-Based Surgical Imaging,ed adaptive histogram equalization algorithm, Hessian matrix construction and region of interest function on the AMD-Xilinx’s Kria KV260 board. It outlines optimizations undertaken to reduce the BRAMs by 38%, DSP48 blocks by 80%, flip-flops by 33% and LUTs by 36%, thus creating a design operating at 121 FPS.
20#
發(fā)表于 2025-3-25 01:58:18 | 只看該作者
An Introduction to Oceanography,ion of on-chip resources. At last, a custom pipelined strategy is presented for . to get a better simulation performance. Compared with a floating-point implementation based on NVIDIA 28080ti GPUs, our design based on Xilinx U200 FPGA is 1.2 times better.
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