Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Xiaocong Du"'
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 6, Iss 1, Pp 27-35 (2020)
Resistive random access memory (RRAM) is a promising technology for energy-efficient neuromorphic accelerators. However, when a pretrained deep neural network (DNN) model is programmed to an RRAM array for inference, the model suffers from accuracy d
Externí odkaz:
https://doaj.org/article/ed9f39b1d39d4305b2941cd434d8fb3d
Autor:
Gokul Krishnan, Li Yang, Jingbo Sun, Jubin Hazra, Xiaocong Du, Maximilian Liehr, Zheng Li, Karsten Beckmann, Rajiv V. Joshi, Nathaniel C. Cady, Deliang Fan, Yu Cao
Publikováno v:
IEEE Transactions on Computers. 71:2740-2752
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f90edb377535df9f57fce9e66ef63fd4
https://doi.org/10.1109/tc.2022.3174585
https://doi.org/10.1109/tc.2022.3174585
Autor:
Xiaocong Du
Publikováno v:
Lecture Notes in Electrical Engineering ISBN: 9789819914272
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::23539551967a3b10d3947bf02f2c465c
https://doi.org/10.1007/978-981-99-1428-9_215
https://doi.org/10.1007/978-981-99-1428-9_215
Publikováno v:
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, Vol 6, Iss 1, Pp 27-35 (2020)
Resistive random access memory (RRAM) is a promising technology for energy-efficient neuromorphic accelerators. However, when a pretrained deep neural network (DNN) model is programmed to an RRAM array for inference, the model suffers from accuracy d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5cadc87b9249320474618473990cb31
https://doi.org/10.1109/jxcdc.2020.2987605
https://doi.org/10.1109/jxcdc.2020.2987605
Publikováno v:
Findings of the Association for Computational Linguistics: NAACL 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bcfd8b3e906386ef5bec2b485361f887
https://doi.org/10.18653/v1/2022.findings-naacl.38
https://doi.org/10.18653/v1/2022.findings-naacl.38
Publikováno v:
IJCNN
Continual learning, the capability to learn new knowledge from streaming data without forgetting the previous knowledge, is a critical requirement for dynamic learning systems, especially for emerging edge devices such as self-driving cars and drones
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc82647afdd4f2e9dbbcd7ccb6706593
https://doi.org/10.1109/ijcnn52387.2021.9534079
https://doi.org/10.1109/ijcnn52387.2021.9534079
Autor:
Karsten Beckmann, Maximilian Liehr, Yu Cao, Xiaocong Du, Gokul Krishnan, Nathaniel C. Cady, Rajiv V. Joshi, Jubin Hazra
Publikováno v:
2021 5th IEEE Electron Devices Technology & Manufacturing Conference (EDTM).
Resistive random-access memory (RRAM)-based in-memory computing (IMC) architecture offers an energy-efficient solution for DNN acceleration. Yet, its performance is limited by device non-idealities, circuit precision, on-chip interconnection, and alg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e61f6a11a464138b7dbec05b8cd2c83b
https://doi.org/10.1109/edtm50988.2021.9421057
https://doi.org/10.1109/edtm50988.2021.9421057
Autor:
Karsten Beckmann, Rajiv V. Joshi, Gokul Krishnan, Yu Cao, Zheng Li, Jubin Hazra, Xiaocong Du, Maximilian Liehr, Jingbo Sun, Nathaniel C. Cady
Publikováno v:
IRPS
Resistive random-access memory (RRAM)-based in-memory computing (IMC) architectures offer an energy-efficient solution for DNN acceleration. However, the performance of RRAM-based IMC is limited by device nonidealities, ADC precision, and algorithm p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e8283efa6205636bff49f3233d258d7
https://doi.org/10.1109/irps46558.2021.9405092
https://doi.org/10.1109/irps46558.2021.9405092
Publikováno v:
ICMLA
Trajectory prediction, an emerging application of spatial-temporal graph, is extremely critical in dynamic applications such as autonomous vehicles and robots. However, the diversity of trajectories and the modeling of mutual relations make it diffic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::662298608828b2f8116daef470fc8b97
https://doi.org/10.1109/icmla51294.2020.00233
https://doi.org/10.1109/icmla51294.2020.00233
Publikováno v:
IJCAI
Scopus-Elsevier
Scopus-Elsevier
Training of deep Convolution Neural Networks (CNNs) requires a tremendous amount of computation and memory and thus, GPUs are widely used to meet the computation demands of these complex training tasks. However, lacking the flexibility to exploit arc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d0dd1b9da97384c8e37ab562ef7e0a3
https://doi.org/10.24963/ijcai.2020/755
https://doi.org/10.24963/ijcai.2020/755