Zobrazeno 1 - 5
of 5
pro vyhledávání: '"José Romero Hung"'
Autor:
José Romero Hung, Chao Li, Taolei Wang, Jinyang Guo, Pengyu Wang, Chuanming Shao, Jing Wang, Guoyong Shi, Xiangwen Liu, Hanqing Wu
Publikováno v:
ACM Transactions on Design Automation of Electronic Systems. 28:1-27
Despite the extraordinary applicative potentiality that dynamic graph inference may entail, its practical-physical implementation has been a topic seldom explored in literature. Although graph inference through neural networks has received plenty of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::138708c12426fe6a054a9510db0980aa
https://doi.org/10.1145/3524124
https://doi.org/10.1145/3524124
Publikováno v:
Frontiers of Computer Science. 17
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::abc0b6610b22042aa81661798f40959e
https://doi.org/10.1007/s11704-022-2127-0
https://doi.org/10.1007/s11704-022-2127-0
Publikováno v:
ACM Transactions on Reconfigurable Technology and Systems. 14:1-23
ACE-GCN is a fast and resource/energy-efficient FPGA accelerator for graph convolutional embedding under data-driven and in-place processing conditions. Our accelerator exploits the inherent power law distribution and high sparsity commonly exhibited
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7b126026e9c494c29ad399c0a2c47075
https://doi.org/10.1145/3470536
https://doi.org/10.1145/3470536
Autor:
José Romero Hung
ACE-GCN is a fast and resource/energy-efficient FPGA accelerator for graph convolutional embedding underdata-driven and in-place processing conditions. Our accelerator exploits the inherent power law distributionand high sparsity commonly exhibited b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d4718148f5f22e0386fbbb84e03c2cf6
https://doi.org/10.31219/osf.io/qcd45
https://doi.org/10.31219/osf.io/qcd45
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