Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Tolulope A. Odetola"'
Autor:
Adewale A. Adeyemo, Jonathan J. Sanderson, Tolulope A. Odetola, Faiq Khalid, Syed Rafay Hasan
Publikováno v:
IEEE Access, Vol 11, Pp 10520-10534 (2023)
With significant potential improvement in device-to-device (D2D) communication due to improved wireless link capacity (e.g., 5G and NextG systems), a collaboration of multiple edge devices (called horizontal collaboration (HC)) is becoming a reality
Externí odkaz:
https://doaj.org/article/536b11fc1dbe4b218a15c75e5ac939a4
Publikováno v:
IEEE Access, Vol 9, Pp 115370-115387 (2021)
Convolutional Neural Networks (CNN) have shown impressive performance in computer vision, natural language processing, and many other applications, but they exhibit high computations and substantial memory requirements. To address these limitations,
Externí odkaz:
https://doaj.org/article/829ea904532f4b6fa64a4ef46283d46e
Publikováno v:
Microprocessors and Microsystems. 100:104827
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bbd82e34e4867aee1aa8b8c5588ab535
https://doi.org/10.1016/j.micpro.2023.104827
https://doi.org/10.1016/j.micpro.2023.104827
Publikováno v:
2022 IEEE International Symposium on Hardware Oriented Security and Trust (HOST).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7142e966a52d50974e35705bb350eb10
https://doi.org/10.1109/host54066.2022.9840017
https://doi.org/10.1109/host54066.2022.9840017
Deep learning applications have achieved great success in numerous real-world applications. Deep learning models, especially Convolution Neural Networks (CNN) are often prototyped using FPGA because it offers high power efficiency and reconfigurabili
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa68bdffc3de1bd37fad8c43647f144a
http://arxiv.org/abs/2202.09461
http://arxiv.org/abs/2202.09461
Publikováno v:
SN Computer Science. 3
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa62bf6c5ba6dd0b0d3a3cc5499941e7
https://doi.org/10.1007/s42979-021-00954-5
https://doi.org/10.1007/s42979-021-00954-5
Publikováno v:
MWSCAS
In this paper, dynamic deployment of Convolutional Neural Network (CNN) architecture is proposed utilizing only IoT-level devices. By partitioning and pipelining the CNN, it horizontally distributes the computation load among resource-constrained dev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f63b7625bcadea6dff115cd7c1070f69
https://doi.org/10.1109/mwscas47672.2021.9531765
https://doi.org/10.1109/mwscas47672.2021.9531765
Publikováno v:
MWSCAS
Convolutional Neural Networks (CNNs) have demonstrated impressive performance in recent times and have shown a wide range of applicability. The deployment of CNNs on resource-constrained edge devices for inference still proves challenging due to the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a33644fa2778ebe83bfa0447447f4df7
https://doi.org/10.1109/mwscas47672.2021.9531783
https://doi.org/10.1109/mwscas47672.2021.9531783
Autor:
Tolulope A. Odetola, Syed Rafay Hasan
Publikováno v:
ISCAS
Security of inference phase deployment of Convolutional neural network (CNN) into resource constrained embedded systems (e.g. low end FPGAs) is a growing research area. Using secure practices, third party FPGA designers can be provided with no knowle
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a18a54423c72c5a985c9cafea688631
http://arxiv.org/abs/2103.09327
http://arxiv.org/abs/2103.09327
Publikováno v:
MWSCAS
The traditional convolution neural networks (CNN) have several drawbacks like the "Picasso effect" and the loss of information by the pooling layer. The Capsule network (CapsNet) was proposed to address these challenges because its architecture can e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e4df834dd2d37a46fcf2a48ed3a66aaf