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Titlebook: Embedded Computer Systems: Architectures, Modeling, and Simulation; 22nd International C Alex Orailoglu,Marc Reichenbach,Matthias Jung Conf

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書目名稱Embedded Computer Systems: Architectures, Modeling, and Simulation
副標(biāo)題22nd International C
編輯Alex Orailoglu,Marc Reichenbach,Matthias Jung
視頻videohttp://file.papertrans.cn/308/307880/307880.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Embedded Computer Systems: Architectures, Modeling, and Simulation; 22nd International C Alex Orailoglu,Marc Reichenbach,Matthias Jung Conf
描述This book constitutes the proceedings of the 22.st. International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021, which took place in July 2022 in Samos, Greece.. The 21 full papers presented in this volume were carefully reviewed and selected from 44 submissions. The papers are organized in topics as follows: High level synthesis; memory systems; processor architecture; embedded software systems and beyond; deep learning optimization; extra-functional property estimation; innovative architectures and tools for security; european research projects on digital systems, services, and platforms.
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; computer hardware; computer networks; computer programming; computer science; co
版次1
doihttps://doi.org/10.1007/978-3-031-15074-6
isbn_softcover978-3-031-15073-9
isbn_ebook978-3-031-15074-6Series 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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EasyHBM: Simple and?Fast HBM Access for?FPGAs Using High-Level-Synthesistime-to-market than traditional RTL designs. HLS also allows developers with less in-depth hardware design knowledge to employ FPGA accelerators for their applications. However, like traditional CPUs, these designs often suffer from an insufficient memory bandwidth to provide data to all computation
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Tagged Geometric History Length Access Interval Prediction for?Tightly Coupled Memory Systemsefficiency and software flexibility. On the one hand, memory sharing improves area utilization, but on the other hand, this can lead to a performance degradation due to an increase in access conflicts. To mitigate the associated performance penalty, access interval prediction (AIP) has been proposed
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