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DASIP 2024: Munich, Germany

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Tiago Dias, Paola Busia:
Design and Architectures for Signal and Image Processing - 17th International Workshop, DASIP 2024, Munich, Germany, January 17-19, 2024, Proceedings. Lecture Notes in Computer Science 14622, Springer 2024, ISBN 978-3-031-62873-3

Specialized Hardware Architectures for Signal and Image Processing

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  - conf/dasip/GevigneyHBNDA24
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Flavie Durand de Gevigney, Julien Heulot, Eric Bazin, Jean-François Nezan, Mickaël Dardaillon, Slaheddine Aridhi:
A Highly Configurable Platform for Advanced PPG Analysis. 3-14
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Matteo Antonio Scrugli, Gianluca Leone, Paola Busia, Paolo Meloni:
sEMG-Based Gesture Recognition with Spiking Neural Networks on Low-Power FPGA. 15-26
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  - conf/dasip/SkarmanG24
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Frans Skarman, Oscar Gustafsson:
Scalable FPGA Implementation of Dynamic Programming for Optimal Control of Hybrid Electrical Vehicles. 27-39

Optimization Approaches for Efficient Deployment of Signal and Image Processing Applications

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Quentin Milot, Mickaël Dardaillon, Justine Bonnot, Daniel Ménard:
Wordlength Optimization for Custom Floating-Point Systems. 43-55
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  - conf/dasip/WangGMNDO24
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Sunrise Wang, Nicolas Gac, Hugo Miomandre, Jean-François Nezan, Karol Desnos, François Orieux:
An Initial Framework for Prototyping Radio-Interferometric Imaging Pipelines. 56-67
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Joseph W. Faye, Naouel Haggui, Florent Kermarrec, Kevin J. M. Martin, Shuvra S. Bhattacharyya, Jean-François Nezan, Maxime Pelcat:
Scratchy: A Class of Adaptable Architectures with Software-Managed Communication for Edge Streaming Applications. 68-79

Digital Signal Processing Design for Reconfigurable Systems

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Chilankamol Sunny, Satyajit Das, Kevin J. M. Martin, Philippe Coussy:
Standalone Nested Loop Acceleration on CGRAs for Signal Processing Applications. 83-95
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Zhuoer Li, Sébastien Bilavarn:
Improving the Energy Efficiency of CNN Inference on FPGA Using Partial Reconfiguration. 96-109
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Kamil Jeziorek, Piotr Wzorek, Krzysztof Blachut, Andréa Pinna, Tomasz Kryjak:
Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-Based Vision. 110-122

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