Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Hyeokjun Choe"'
Publikováno v:
IEEE Access, Vol 6, Pp 49601-49610 (2018)
Today’s data centers have various computing and storage devices for processing a myriad of data, and they generally consume a considerable amount of electrical energy. This paper proposes a smart grid-inspired methodology to observe and profile the
Externí odkaz:
https://doaj.org/article/64e994f1e2894130b057fc081bac97de
Publikováno v:
IJCAI
Despite the increasing interest in neural architecture search (NAS), the significant computational cost of NAS is a hindrance to researchers. Hence, we propose to reduce the cost of NAS using proxy data, i.e., a representative subset of the target da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a02d2f1ba6e19dea8b82c16b20e8d26
http://arxiv.org/abs/2106.04784
http://arxiv.org/abs/2106.04784
Publikováno v:
IEEE Access, Vol 6, Pp 49601-49610 (2018)
Today’s data centers have various computing and storage devices for processing a myriad of data, and they generally consume a considerable amount of electrical energy. This paper proposes a smart grid-inspired methodology to observe and profile the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3904c3e83787aa49fc28607c7c4556c4
https://doi.org/10.1109/access.2018.2868469
https://doi.org/10.1109/access.2018.2868469
Publikováno v:
DAC
The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability. Recently, conversion of a trained deep neural network to an SNN has improved the accuracy of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a93ef429986bd772f66c83ab38b8f19
https://doi.org/10.1145/3316781.3317822
https://doi.org/10.1145/3316781.3317822
Real-Time Anomalous Branch Behavior Inference with a GPU-inspired Engine for Machine Learning Models
Publikováno v:
DATE
Attacks on embedded devices are likely to occur any time in unexpected manners. Thus, the defense systems based on fixed sets of rules will easily be subverted by such unexpected, unknown attacks. Learning-based anomaly detection may potentially prev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5b10e25bfa02059b7dea37255ea6a54
https://doi.org/10.23919/date.2019.8714807
https://doi.org/10.23919/date.2019.8714807
Publikováno v:
DAC: Annual ACM/IEEE Design Automation Conference; 2019, Issue 56, p499-504, 6p