Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Giorgos Armeniakos"'
Printed electronics (PE) promises on-demand fabrication, low non-recurring engineering costs, and sub-cent fabrication costs. It also allows for high customization that would be infeasible in silicon, and bespoke architectures prevail to improve the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4286d3799e6131e90cdfbe8dcb0caca
http://arxiv.org/abs/2303.08255
http://arxiv.org/abs/2303.08255
Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware due to its additive manufacturing process, enabling machine learning (ML) applications for domains that feature ultra-low cost, conformity, and non-toxicity requirements that si
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d215de110ba55fb8fc4153811184e35
http://arxiv.org/abs/2302.14576
http://arxiv.org/abs/2302.14576
Publikováno v:
Electronics, Vol 11, Iss 39, p 39 (2022)
Electronics; Volume 11; Issue 1; Pages: 39
Electronics; Volume 11; Issue 1; Pages: 39
Every new generation of wireless communication standard aims to improve the overall performance and quality of service (QoS), compared to the previous generations. Increased data rates, numbers and capabilities of connected devices, new applications,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75297c35980569ba9f60f6bd0b414002
https://www.mdpi.com/2079-9292/11/1/39
https://www.mdpi.com/2079-9292/11/1/39
Deep Neural Networks (DNNs) are very popular because of their high performance in various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have brought beyond human accuracy in many tasks, but at the cost of high computational co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e3a76fa663945ba119f2a523f096a91e
Publikováno v:
MOCAST
Modern mobile communication systems utilize increased bandwidth to provide advanced network performance and connectivity, all while their most computationally-intensive functions must be accelerated within the limited power envelope of embedded devic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::93c4cd23232b3233eab6198ebb58fcb0
https://doi.org/10.1109/mocast52088.2021.9493421
https://doi.org/10.1109/mocast52088.2021.9493421