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pro vyhledávání: '"Suyeon Hur"'
Autor:
Suyeon Hur, Seongmin Na, Dongup Kwon, Joonsung Kim, Andrew Boutros, Eriko Nurvitadhi, Jangwoo Kim
Publikováno v:
ACM Transactions on Architecture and Code Optimization. 20:1-24
Deep neural networks (DNNs) have become key solutions in the natural language processing (NLP) domain. However, the existing accelerators customized for their narrow target models cannot support diverse NLP models. Therefore, naively running complex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb549db15f166174430bdb3d775bfe17
https://doi.org/10.1145/3564606
https://doi.org/10.1145/3564606
Akademický článek
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Publikováno v:
PACT
Emerging natural language processing (NLP) models have become more complex and bigger to provide more sophisticated NLP services. Accordingly, there is also a strong demand for scalable and flexible computer infrastructure to support these large-scal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dfa4e0dea956e7554ecfde4dd82cff1b
https://doi.org/10.1109/pact52795.2021.00013
https://doi.org/10.1109/pact52795.2021.00013
Publikováno v:
DAC
The increasing size of recurrent neural networks (RNNs) makes it hard to meet the growing demand for real-time AI services. For low-latency RNN serving, FPGA-based accelerators can leverage specialized architectures with optimized dataflow. However,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2144985703ab89292df0df93cab2088a
https://doi.org/10.1109/dac18072.2020.9218528
https://doi.org/10.1109/dac18072.2020.9218528
Publikováno v:
DAC: Annual ACM/IEEE Design Automation Conference; 2020, Issue 57, p1142-1147, 6p