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Titlebook: Computer Engineering and Technology; 16th National Confer Weixia Xu,Liquan Xiao,Chengyi Zhang Conference proceedings 2013 Springer-Verlag B

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發(fā)表于 2025-3-25 06:42:33 | 只看該作者
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發(fā)表于 2025-3-25 09:26:30 | 只看該作者
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發(fā)表于 2025-3-25 14:48:46 | 只看該作者
Tax Incentives for the Art Markett the Nussinov algorithm on hardware. We implement our work on Xilinx FPGA, the total clock cycles to accomplish the algorithm is about half of using software in serial way, and we also partly resolve the limitation of fixed length requirement of existing hardware implementation with an efficient resource usage.
24#
發(fā)表于 2025-3-25 18:23:29 | 只看該作者
Tax Incentives Crossing Bordersmance and strong generality of the system. Loop unrolling approach is used to eliminate the data hazards caused by pipelining. The hardware usage of the configurable architecture is fixed for any kind of wavelet functions.
25#
發(fā)表于 2025-3-25 22:41:38 | 只看該作者
26#
發(fā)表于 2025-3-26 02:33:13 | 只看該作者
ADL and High Performance Processor Designenches and compiler utilities. This paper analyzes and compares three processor description languages. We also give the disadvantages of modern ADL when used in high performance processor design and give some suggestions for further ADL development.
27#
發(fā)表于 2025-3-26 05:54:22 | 只看該作者
A Hardware Implementation of Nussinov RNA Folding Algorithmt the Nussinov algorithm on hardware. We implement our work on Xilinx FPGA, the total clock cycles to accomplish the algorithm is about half of using software in serial way, and we also partly resolve the limitation of fixed length requirement of existing hardware implementation with an efficient resource usage.
28#
發(fā)表于 2025-3-26 11:42:08 | 只看該作者
29#
發(fā)表于 2025-3-26 13:13:58 | 只看該作者
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發(fā)表于 2025-3-26 20:04:41 | 只看該作者
Hardware Architecture for the Parallel Generation of Long-Period Random Numbers Using MT Methodtiple samples each period. This performance let us obtain higher throughput than the non-parallelization architecture and software. The samples generated by our design are applied to a Monte Carlo simulation for estimating the value of ., and we achieve the accuracy of 99.99%.
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