Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Yangjie Qi"'
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 42:1648-1661
There is increasing demand for specialized hardware for training deep neural networks, both in edge/IoT environments and in high-performance computing systems. The design space of such hardware is very large due to the wide range of processing archit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd32c83679018294adae613926d8170c
https://doi.org/10.1109/tcad.2022.3203959
https://doi.org/10.1109/tcad.2022.3203959
Autor:
Yangjie Qi, Chang Liu
Publikováno v:
ES Materials & Manufacturing.
This review provide an A to Z and in-depth discussion of deep learning to be used in material science and engineering, especially for material scientist.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a5a1421963db54accb23c2dfac79319
https://doi.org/10.30919/esmm5f426
https://doi.org/10.30919/esmm5f426
Publikováno v:
IJCNN
Compact online learning architectures can be used to enhance internet of things devices, allowing them to learn directly on received data instead of sending data to a remote server for learning. This saves communication energy and enhances privacy an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7fd98a281b380c6dca9a362392a9cd8d
https://doi.org/10.1109/ijcnn48605.2020.9206929
https://doi.org/10.1109/ijcnn48605.2020.9206929
Publikováno v:
IJCNN
Specialized ultra-low power deep learning architectures with on-chip training capability can be useful in variety of applications that require adaptability. This paper presents such a processor design, Socrates-D 2.0, a multicore architecture for dee
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d6ad364363e5524824f69a60bf4d66b
https://doi.org/10.1109/ijcnn.2018.8489378
https://doi.org/10.1109/ijcnn.2018.8489378
Publikováno v:
ICRC
Compact online learning architectures could be used to enhance internet of things devices to allow them to learn directly based on data being received instead of having to ship data to a remote server for learning. This saves communications energy an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1185463b0425e8909e9f7313a9be2940
https://doi.org/10.1109/icrc.2017.8123668
https://doi.org/10.1109/icrc.2017.8123668
Publikováno v:
NAECON 2014 - IEEE National Aerospace and Electronics Conference.
The Interest in specialized neuromorphic computing architectures has been increasing recently, and several applications have been shown to be capable of being accelerated on such a platform. This paper describes the implementation of a multicore digi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2020d65b652d89e0325d7734a50da96c
https://doi.org/10.1109/naecon.2014.7045812
https://doi.org/10.1109/naecon.2014.7045812