Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Kazim Ergun"'
Publikováno v:
IEEE Internet of Things Journal. 10:3864-3889
Publikováno v:
IEEE Transactions on Network and Service Management. 20:787-799
𝖧𝗒𝖣𝖱𝖤𝖠: Utilizing Hyperdimensional Computing for a More Robust and Efficient Machine Learning System
Publikováno v:
ACM Transactions on Embedded Computing Systems. 21:1-25
Today’s systems rely on sending all the data to the cloud and then using complex algorithms, such as Deep Neural Networks, which require billions of parameters and many hours to train a model. In contrast, the human brain can do much of this learni
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Autor:
Rishikanth Chandrasekaran, Kazim Ergun, Jihyun Lee, Dhanush Nanjunda, Jaeyoung Kang, Tajana Rosing
Publikováno v:
Proceedings of the 59th ACM/IEEE Design Automation Conference.
Autor:
Emily Ekaireb, Xiaofan Yu, Kazim Ergun, Quanling Zhao, Kai Lee, Muhammad Huzaifa, Tajana Rosing
Publikováno v:
Proceedings of the WNS3 2022.
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 39:3918-3930
Recent advances in low-power long-range communication schemes such as LoRa have opened up new potentials in large-scale Internet-of-Things (IoT) applications, especially environmental monitoring. However, the versatile environment and the long travel
Publikováno v:
Ad Hoc Networks. 132:102869
Publikováno v:
DATE
Today's systems, especially in the age of federated learning, rely on sending all the data to the cloud, and then use complex algorithms, such as Deep Neural Networks, which require billions of parameters and many hours to train a model. In contrast,
Publikováno v:
ASP-DAC
The Internet of Things (IoT) systems, as any electronic or mechanical system, are prone to failures. Hard failures in hardware due to aging and degradation are particularly important since they are irrecoverable, requiring maintenance for the replace