Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Hyemi Min"'
Publikováno v:
Proceedings of the 21st ACM/IEEE International Symposium on Code Generation and Optimization.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8cacc487698797901f2f31a3742d8d62
https://doi.org/10.1145/3579990.3580025
https://doi.org/10.1145/3579990.3580025
Publikováno v:
Proceedings of the 2021 International Conference on Compilers, Architectures, and Synthesis for Embedded Systems.
Generating an optimal execution plan for a given convolutional neural network (CNN) and a parameterizable hardware accelerator is a challenge.We present a framework that finds an execution plan that maximizes throughput for a given network and a spec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c737a64cd7cb605824eb2548eb2717e3
https://doi.org/10.1145/3451939.3477593
https://doi.org/10.1145/3451939.3477593
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
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
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 10:1534-1541
A particle simulation method is introduced in which two kinds of particle models are used in one device. A conventional Monte Carlo particle model is used in the region where nonstatic effects are evident, and a particle model based on Langevin's equ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96a6334864d4f215a27cea4ad0f80e2b
https://doi.org/10.1109/43.103503
https://doi.org/10.1109/43.103503
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems; Nov2018, Vol. 37 Issue 11, p2301-2310, 10p