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Titlebook: Computer Engineering and Technology; 21st CCF Conference, Weixia Xu,Liquan Xiao,Zhenzhen Zhu Conference proceedings 2018 Springer Nature Si

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發(fā)表于 2025-3-21 19:47:46 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computer Engineering and Technology
副標(biāo)題21st CCF Conference,
編輯Weixia Xu,Liquan Xiao,Zhenzhen Zhu
視頻videohttp://file.papertrans.cn/234/233538/233538.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Computer Engineering and Technology; 21st CCF Conference, Weixia Xu,Liquan Xiao,Zhenzhen Zhu Conference proceedings 2018 Springer Nature Si
描述This book constitutes the refereed proceedings of the 21st CCF Conference on Computer Engineering and Technology, NCCET 2017, held in Xiamen, China, in August 2017.?.The 13 full papers presented were carefully reviewed and selected from 108 submissions. They address topics such as processor architecture; application specific processors; computer application and software optimization; technology on the horizon..
出版日期Conference proceedings 2018
關(guān)鍵詞computer architecture; integrated circuits; integrated circuit layout; integrated circuit design; printe
版次1
doihttps://doi.org/10.1007/978-981-10-7844-6
isbn_softcover978-981-10-7843-9
isbn_ebook978-981-10-7844-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Singapore Pte Ltd. 2018
The information of publication is updating

書目名稱Computer Engineering and Technology影響因子(影響力)




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Tax Havens and Intermediate Companiespaper. The anti-harsh environment reinforcement chassis is taken environmental tests to make sure that it has good reliability and integrated protection capabilities, which provides a reference for similar engineering design.
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Tax Havens and Intermediate Companiesed classification model can achieve AUC?=?0.9973 in best case and the influence by the data size or kernel size is very little. Moreover, by comparison with other CNNs trained with software-based features, it is indicated that the proposed model has higher accuracy than the other ones.
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ACCDSE: A Design Space Exploration Framework for Convolutional Neural Network Accelerator, data precision for inference of LeNet. By theoritical analysis, the ACCDSE framework can obtain optimal matrix tiling parameters. Without decreasing the classification accuracy, the power consumption can be reduced by 33.57% and the storage can be reduced by 41.47% after weight pruning.
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Malware Detection with Convolutional Neural Network Using Hardware Events,ed classification model can achieve AUC?=?0.9973 in best case and the influence by the data size or kernel size is very little. Moreover, by comparison with other CNNs trained with software-based features, it is indicated that the proposed model has higher accuracy than the other ones.
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