Zobrazeno 1 - 10 of 24 pro vyhledávání: '"Fasfous, Nael"'
1
Akademický článek
HW-Flow: A Multi-Abstraction Level HW-CNN Codesign Pruning Methodology
Autor: Vemparala, Manoj-Rohit, Fasfous, Nael, Frickenstein, Alexander, Valpreda, Emanuele, Camalleri, Manfredi, Zhao, Qi, Unger, Christian, Nagaraja, Naveen-Shankar, Martina, Maurizio, Stechele, Walter
Publikováno v: Leibniz Transactions on Embedded Systems, Vol 8, Iss 1, Pp 03:1-03:30 (2022)
Convolutional neural networks (CNNs) have produced unprecedented accuracy for many computer vision problems in the recent past. In power and compute-constrained embedded platforms, deploying modern CNNs can present many challenges. Most CNN architect
Externí odkaz: https://doaj.org/article/3ca79e6033da4d998bfed4cea34f29c3
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2
Report
BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks
Autor: Vemparala, Manoj Rohit, Frickenstein, Alexander, Fasfous, Nael, Frickenstein, Lukas, Zhao, Qi, Kuhn, Sabine, Ehrhardt, Daniel, Wu, Yuankai, Unger, Christian, Nagaraja, Naveen Shankar, Stechele, Walter
Deploying convolutional neural networks (CNNs) for embedded applications presents many challenges in balancing resource-efficiency and task-related accuracy. These two aspects have been well-researched in the field of CNN compression. In real-world a
Externí odkaz: http://arxiv.org/abs/2103.08031
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3
Report
BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices
Autor: Fasfous, Nael, Vemparala, Manoj-Rohit, Frickenstein, Alexander, Frickenstein, Lukas, Stechele, Walter
Face masks have long been used in many areas of everyday life to protect against the inhalation of hazardous fumes and particles. They also offer an effective solution in healthcare for bi-directional protection against air-borne diseases. Wearing an
Externí odkaz: http://arxiv.org/abs/2102.03456
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4
Report
L2PF -- Learning to Prune Faster
Autor: Vemparala, Manoj-Rohit, Fasfous, Nael, Frickenstein, Alexander, Moraly, Mhd Ali, Jamal, Aquib, Frickenstein, Lukas, Unger, Christian, Nagaraja, Naveen-Shankar, Stechele, Walter
Various applications in the field of autonomous driving are based on convolutional neural networks (CNNs), especially for processing camera data. The optimization of such CNNs is a major challenge in continuous development. Newly learned features mus
Externí odkaz: http://arxiv.org/abs/2101.02663
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5
Report
ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks
Autor: Frickenstein, Alexander, Vemparala, Manoj-Rohit, Fasfous, Nael, Hauenschild, Laura, Nagaraja, Naveen-Shankar, Unger, Christian, Stechele, Walter
Closing the gap between the hardware requirements of state-of-the-art convolutional neural networks and the limited resources constraining embedded applications is the next big challenge in deep learning research. The computational complexity and mem
Externí odkaz: http://arxiv.org/abs/2007.13384
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7
Hardware-Software Co-Design of Deep Neural Networks: From Handcrafted to Automated Design and Deployment
Autor: Al-Fasfous, Nael Y. A.
This work reinterprets concepts from the mature discipline of hardware-software (HW-SW) co-design to deploy DNNs on edge devices. The resulting co-designed algorithms tackled autonomous driving problems with high efficiency, enabled power-forecasting
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______518::c289fc1723266cb445be5810f78a3b54
https://mediatum.ub.tum.de/doc/1656745/document.pdf
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8
Akademický článek
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