Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Gil Shomron"'
Publikováno v:
Mathematics, Vol 10, Iss 19, p 3679 (2022)
Convolutional neural networks (CNNs) offer significant advantages when used in various image classification tasks and computer vision applications. CNNs are increasingly deployed in environments from edge and Internet of Things (IoT) devices to high-
Externí odkaz:
https://doaj.org/article/dccaaa08f66b483e943caffad21c6e7a
Autor:
Freddy Gabbay, Gil Shomron
Publikováno v:
Mathematics, Vol 9, Iss 20, p 2612 (2021)
Convolutional Neural Networks (CNNs) are broadly used in numerous applications such as computer vision and image classification. Although CNN models deliver state-of-the-art accuracy, they require heavy computational resources that are not always aff
Externí odkaz:
https://doaj.org/article/0e17c52d2ffa40f49d6877c9eddaea60
Publikováno v:
IEEE Computer Architecture Letters. 22:49-52
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e1ec2c2e8f88487a03d33ab75d57e9a
https://doi.org/10.1109/lca.2023.3275909
https://doi.org/10.1109/lca.2023.3275909
Autor:
Gil Shomron, Freddy Gabbay
Publikováno v:
Mathematics, Vol 9, Iss 2612, p 2612 (2021)
Mathematics
Volume 9
Issue 20
Mathematics
Volume 9
Issue 20
Convolutional Neural Networks (CNNs) are broadly used in numerous applications such as computer vision and image classification. Although CNN models deliver state-of-the-art accuracy, they require heavy computational resources that are not always aff
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71c52dcc7c6bfaf0eff8349ddb457ef0
https://www.mdpi.com/2227-7390/9/20/2612
https://www.mdpi.com/2227-7390/9/20/2612
Publikováno v:
IEEE Computer Architecture Letters. 18:99-102
Systolic arrays (SAs) are highly parallel pipelined structures capable of executing various tasks such as matrix multiplication and convolution. They comprise a grid of usually homogeneous processing units (PUs) that are responsible for the multiply-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31251bcecac695e45dd546299374509c
https://doi.org/10.1109/lca.2019.2924007
https://doi.org/10.1109/lca.2019.2924007
Autor:
Uri Weiser, Gil Shomron
Publikováno v:
MICRO
Deep neural networks (DNNs) are known for their inability to utilize underlying hardware resources due to hardware susceptibility to sparse activations and weights. Even in finer granularities, many of the non-zero values hold a portion of zero-value
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::082bdb732349401bdb70f0a41ed22d58
http://arxiv.org/abs/2004.09309
http://arxiv.org/abs/2004.09309
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030586065
ECCV (10)
ECCV (10)
Convolutional neural networks (CNNs) introduce state-of-the-art results for various tasks with the price of high computational demands. Inspired by the observation that spatial correlation exists in CNN output feature maps (ofms), we propose a method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a832064b8de5fb6d2a1767eb596015c
https://doi.org/10.1007/978-3-030-58607-2_14
https://doi.org/10.1007/978-3-030-58607-2_14
Publikováno v:
IEEE Computer Architecture Letters. 16:68-71
Memory hierarchies in modern computing systems work well for workloads that exhibit temporal data locality. Data that is accessed frequently is brought closer to the computing cores, allowing faster access times, higher bandwidth, and reduced transmi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7a5606ce0e3e28c0be8e6e55a22b99d8
https://doi.org/10.1109/lca.2016.2520927
https://doi.org/10.1109/lca.2016.2520927
Autor:
Uri Weiser, Gil Shomron
Convolutional neural networks (CNNs) are a widely used form of deep neural networks, introducing state-of-the-art results for different problems such as image classification, computer vision tasks, and speech recognition. However, CNNs are compute in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f14ca209c1bde56641816d5277999aa
http://arxiv.org/abs/1807.10598
http://arxiv.org/abs/1807.10598