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Titlebook: Algorithm-Architecture Matching for Signal and Image Processing; Best papers from Des Guy Gogniat,Dragomir Milojevic,Ahmet Erdogan Book 201

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發(fā)表于 2025-3-21 18:06:41 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Algorithm-Architecture Matching for Signal and Image Processing
期刊簡稱Best papers from Des
影響因子2023Guy Gogniat,Dragomir Milojevic,Ahmet Erdogan
視頻videohttp://file.papertrans.cn/153/152843/152843.mp4
發(fā)行地址Provides last optimization techniques for implementing signal and image processing Highlights advanced techniques for dynamic and partial reconfigurable systems Introduces next generation CAD tools fo
學(xué)科分類Lecture Notes in Electrical Engineering
圖書封面Titlebook: Algorithm-Architecture Matching for Signal and Image Processing; Best papers from Des Guy Gogniat,Dragomir Milojevic,Ahmet Erdogan Book 201
影響因子.Advances in signal and image processing together with increasing computing power are bringing mobile technology closer to applications in a variety of domains like automotive, health, telecommunication, multimedia, entertainment and many others. The development of these leading applications, involving a large diversity of algorithms (e.g. signal, image, video, 3D, communication, cryptography) is classically divided into three consecutive steps: a theoretical study of the algorithms, a study of the target architecture, and finally the implementation. Such a linear design flow is reaching its limits due to intense pressure on design cycle and strict performance constraints. The approach, called Algorithm-Architecture Matching, aims to leverage design flows with a simultaneous study of both algorithmic and architectural issues, taking into account multiple design constraints, as well as algorithm and architecture optimizations, that couldn’t be achieved otherwise if considered separately. Introducing new design methodologies is mandatory when facing the new emerging applications as for example advanced mobile communication or graphics using sub-micron manufacturing technologies or 3D
Pindex Book 2011
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https://doi.org/10.1007/978-3-658-24616-7nt on the reconfigurable resource of a System-on-Chip architecture. An adaptation of the Hopfield model is proposed with a regular reconfiguration of the Artificial Neural Network (ANN). Simulation results, which evaluate the RANN convergence, show significant improvement in comparison with the prev
地板
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Demos und Nation: Begriffser?rterungeny stage in the design flow. We present experimental results in the context of artificial vision for mobile robots and show that our method produces a good trade-off between estimation accuracy and simulation time.
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Algorithm-Architecture Matching for Signal and Image ProcessingBest papers from Des
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1876-1100 separately. Introducing new design methodologies is mandatory when facing the new emerging applications as for example advanced mobile communication or graphics using sub-micron manufacturing technologies or 3D978-94-007-3392-3978-90-481-9965-5Series ISSN 1876-1100 Series E-ISSN 1876-1119
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Pluralistische Demokratietheorieegions. The simplicity of our scheme enables the hardware amenability. Experimental results show that LMMIC achieves superior compression ratios, with the benefits of enabling encoding any number of bands and easy access to any band. We also describe the hardware architecture for this scheme.
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Vergleichende Politikwissenschaftnfigurable area management. Experiments demonstrate improvement of up to 36% in resource utilization over the available reconfigurable resources, 43% in resource gain as compared to static implementation, and an overall configuration overhead of 11% from the total application running time.
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Demos und Nation: Begriffser?rterungen communication cost is increasing from medium to high in modern applications like digital communication and video compression, the proposed method is well-adapted for scheduling these applications over parallel embedded systems.
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