Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Inpyo Bae"'
Publikováno v:
Integration. 67:121-133
A convolutional neural network (CNN) architecture supporting on-device user customization is proposed. The network architecture consists of a large CNN trained on a general data and a smaller augmenting network that can be re-trained on-device using
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27e419197c5ca765dcd749f02945a80e
https://doi.org/10.1016/j.vlsi.2018.11.001
https://doi.org/10.1016/j.vlsi.2018.11.001
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 37:2301-2310
As more and more deep learning tasks are pushed to mobile devices, accelerators for running these networks efficiently gain in importance. We show a that an existing class of general purpose accelerators, modulo-scheduled coarse-grained reconfigurabl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a84284176c81639d5e41a9d7c4e2ad50
https://doi.org/10.1109/tcad.2018.2857278
https://doi.org/10.1109/tcad.2018.2857278
Publikováno v:
ICCAD
Executing deep learning algorithms on mobile embedded devices is challenging because embedded devices usually have tight constraints on the computational power, memory size, and energy consumption while the resource requirements of deep learning algo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ce2476ee420aa8fa4d7b605c1661c754
https://doi.org/10.1145/3240765.3240786
https://doi.org/10.1145/3240765.3240786
Autor:
Barend Harris, Mansureh S. Moghaddam, Duseok Kang, Inpyo Bae, Euiseok Kim, Hyemi Min, Hansu Cho, Sukjin Kim, Bernhard Egger, Soonhoi Ha, Kiyoung Choi
Publikováno v:
2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::048eef4bb56b7b75d83cc77d9028b001
https://doi.org/10.1109/aspdac.2018.8297379
https://doi.org/10.1109/aspdac.2018.8297379
Autor:
Kiyoung Choi, Hyemi Min, Sukjin Kim, Mansureh S. Moghaddam, Barend Harris, Soonhoi Ha, Duseok Kang, Inpyo Bae, Euiseok Kim, Bernhard Egger, Hansu Cho
Publikováno v:
CASES
This paper presents a convolutional neural network architecture that supports transfer learning for user customization. The architecture consists of a large basic inference engine and a small augmenting engine. Initially, both engines are trained usi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::423e322ef8c985a90a6eb1af7daabb28
https://doi.org/10.1145/3125501.3125519
https://doi.org/10.1145/3125501.3125519