Zobrazeno 1 - 5
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pro vyhledávání: '"Meng-Yao Lin"'
Autor:
Meng-Yao Lin, 林孟瑤
106
Memristor-based deep learning accelerators provide a promising solution to improve the energy efficiency of neuromorphic computing systems. How- ever, the electrical properties and crossbar structure of memristors make them sensitive to erro
Memristor-based deep learning accelerators provide a promising solution to improve the energy efficiency of neuromorphic computing systems. How- ever, the electrical properties and crossbar structure of memristors make them sensitive to erro
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/597793
Autor:
Meng-Yao Lin, 林孟堯
101
In the previous study, we have developed a biochip image analysis system, which use the 96 plate hole image. There will be distortions in the image, and result in poor system accuracy. Therefore, this study developed a biochip image analysis
In the previous study, we have developed a biochip image analysis system, which use the 96 plate hole image. There will be distortions in the image, and result in poor system accuracy. Therefore, this study developed a biochip image analysis
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/78801067273218047646
DL-RSIM: A Reliability and Deployment Strategy Simulation Framework for ReRAM-based CNN Accelerators
Autor:
Wei-Ting Lin, Hsiang-Yun Cheng, Chia-Lin Yang, Meng-Yao Lin, Kai Lien, Han-Wen Hu, Hung-Sheng Chang, Hsiang-Pang Li, Meng-Fan Chang, Yen-Ting Tsou, Chin-Fu Nien
Publikováno v:
ACM Transactions on Embedded Computing Systems. 21:1-29
Memristor-based deep learning accelerators provide a promising solution to improve the energy efficiency of neuromorphic computing systems. However, the electrical properties and crossbar structure of memristors make these accelerators error-prone. I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0e759c64b0cf8a777a9131625447809
https://doi.org/10.1145/3507639
https://doi.org/10.1145/3507639
Publikováno v:
ICIP
IoT devices must and will be more intelligent in the future. However, due to the lack of benchmarks representative to the diverse IoT applications, there are limited architecture performance studies on IoT devices. This paper presents IoTBench, the f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb3330ac8c9e2580d572ee1ddaa081f6
https://doi.org/10.1109/icip.2019.8802949
https://doi.org/10.1109/icip.2019.8802949
Autor:
Tzu-Hsien Yang, Hung-Sheng Chang, Wei-Ting Lin, Chia-Lin Yang, Meng-Fan Chang, Hsiang-Yun Cheng, Meng-Yao Lin, Hsiang-Pang Li, I-Ching Tseng, Han-Wen Hu
Publikováno v:
ICCAD
Memristor-based deep learning accelerators provide a promising solution to improve the energy efficiency of neuromorphic computing systems. However, the electrical properties and crossbar structure of memristors make these accelerators error-prone. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::066d334463e354ceee4072b0549408a5
https://doi.org/10.1145/3240765.3240800
https://doi.org/10.1145/3240765.3240800