KR102561799B1 - 디바이스에서 딥러닝 모델의 레이턴시를 예측하는 방법 및 시스템 - Google Patents
디바이스에서 딥러닝 모델의 레이턴시를 예측하는 방법 및 시스템 Download PDFInfo
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- KR102561799B1 KR102561799B1 KR1020220094417A KR20220094417A KR102561799B1 KR 102561799 B1 KR102561799 B1 KR 102561799B1 KR 1020220094417 A KR1020220094417 A KR 1020220094417A KR 20220094417 A KR20220094417 A KR 20220094417A KR 102561799 B1 KR102561799 B1 KR 102561799B1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
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US17/819,281 US12033052B2 (en) | 2021-08-12 | 2022-08-11 | Latency prediction method and computing device for the same |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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KR1020210106527 | 2021-08-12 | ||
KR20210106527 | 2021-08-12 |
Publications (2)
Publication Number | Publication Date |
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KR20230024835A KR20230024835A (ko) | 2023-02-21 |
KR102561799B1 true KR102561799B1 (ko) | 2023-07-31 |
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ID=85200773
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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KR1020220094417A KR102561799B1 (ko) | 2021-08-12 | 2022-07-29 | 디바이스에서 딥러닝 모델의 레이턴시를 예측하는 방법 및 시스템 |
Country Status (2)
Country | Link |
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KR (1) | KR102561799B1 (fr) |
WO (1) | WO2023017884A1 (fr) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
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CN116668475B (zh) * | 2023-05-18 | 2023-12-26 | 尚学仕教育科技(北京)有限公司 | 在线教育操作系统 |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
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KR20210073242A (ko) * | 2019-12-10 | 2021-06-18 | 삼성전자주식회사 | 모델 최적화 방법 및 장치 및 모델 최적화 장치를 포함한 가속기 시스템 |
-
2021
- 2021-08-19 WO PCT/KR2021/011006 patent/WO2023017884A1/fr unknown
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2022
- 2022-07-29 KR KR1020220094417A patent/KR102561799B1/ko active IP Right Grant
Non-Patent Citations (1)
Title |
---|
Matthias Wess et al., "ANNETTE: ACCURATE NEURAL NETWORK EXECUTION TIME ESTIMATION WITH STACKED MODELS," arXiv:2105.03176v1 [cs.LG] 7 May 2021 (2021.05.07.)* |
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Publication number | Publication date |
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KR20230024835A (ko) | 2023-02-21 |
WO2023017884A1 (fr) | 2023-02-16 |
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