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Titlebook: Parallel and Distributed Computing, Applications and Technologies; 23rd International C Hiroyuki Takizawa,Hong Shen,Ryusuke Egawa Conferenc

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發(fā)表于 2025-3-21 17:09:24 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Parallel and Distributed Computing, Applications and Technologies
副標(biāo)題23rd International C
編輯Hiroyuki Takizawa,Hong Shen,Ryusuke Egawa
視頻videohttp://file.papertrans.cn/742/741067/741067.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Parallel and Distributed Computing, Applications and Technologies; 23rd International C Hiroyuki Takizawa,Hong Shen,Ryusuke Egawa Conferenc
描述This book constitutes the proceedings of the 23rd International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2022, which took place in Sendai, Japan, during December 7-9, 2022..The 24 full papers and 16 short papers included in this volume were carefully reviewed and selected from 95 submissions. The papers are categorized into the following topical sub-headings:?Heterogeneous System (1; HPC & AI; Embedded systems & Communication;?Blockchain;?Deep Learning;?Quantum Computing & Programming Language;?Best Papers;?Heterogeneous System (2);?Equivalence Checking & Model checking;?Interconnect;?Optimization (1);?Optimization (2);?Privacy; and Workflow..
出版日期Conference proceedings 2023
關(guān)鍵詞parallel processing systems; artificial intelligence; microprocessor chips; microprogramming; parallel a
版次1
doihttps://doi.org/10.1007/978-3-031-29927-8
isbn_softcover978-3-031-29926-1
isbn_ebook978-3-031-29927-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Towards Priority-Flexible Task Mapping for?Heterogeneous Multi-core NUMA Systems their characteristics. Compared with other methods that do not switch mapping priorities, the proposed method achieves overall performance improvement when dealing with a set of applications with different characteristics.
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An AutoML Based Algorithm for?Performance Prediction in?HPC Systemsnce prediction accuracy results of RMSE (Root Mean Squared Error), MedAE (Median Absolute Error) and MedAPE (Median Absolute Percentage Error) from our experiments demonstrate superior performance of our algorithm compared to the state-of-the-art Auto-Keras framework.
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DEEPFAKE CLI: Accelerated Deepfake Detection Using FPGAsrated it on VCK5000 FPGA, powered by state-of-the-art VC1902 Versal Architecture which uses AI, DSP, and Adaptable Engines for acceleration. We have benchmarked our inference speed with other state-of-the-art inference nodes, got 316.8 FPS on VCK5000 while maintaining 93% Accuracy.
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Parallel and Distributed Computing, Applications and Technologies23rd International C
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