En-Jui Chang, Cheng-Xin Xue, Chetan Deshpande, Gajanan Jedhe, Jenwei Liang, Chih-Chung Cheng, Hung-Wei Lin, Chia-Da Lee, Sushil Kumar, Kim Soon Jway, Zijie Guo, Ritesh Garg, Allen-CL Lu, Chien-Hung Lin, Meng-Han Hsieh, Tsung-Yao Lin, Chih-Cheng Chen. A 12-nm 0.62-1.61 mW Ultra-Low Power Digital CIM-based Deep-Learning System for End-to-End Always-on Vision. In 2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits), Kyoto, Japan, June 11-16, 2023. pages 1-2, IEEE, 2023. [doi]
@inproceedings{ChangXDJLCLLKJG23,
title = {A 12-nm 0.62-1.61 mW Ultra-Low Power Digital CIM-based Deep-Learning System for End-to-End Always-on Vision},
author = {En-Jui Chang and Cheng-Xin Xue and Chetan Deshpande and Gajanan Jedhe and Jenwei Liang and Chih-Chung Cheng and Hung-Wei Lin and Chia-Da Lee and Sushil Kumar and Kim Soon Jway and Zijie Guo and Ritesh Garg and Allen-CL Lu and Chien-Hung Lin and Meng-Han Hsieh and Tsung-Yao Lin and Chih-Cheng Chen},
year = {2023},
doi = {10.23919/VLSITechnologyandCir57934.2023.10185296},
url = {https://doi.org/10.23919/VLSITechnologyandCir57934.2023.10185296},
researchr = {https://researchr.org/publication/ChangXDJLCLLKJG23},
cites = {0},
citedby = {0},
pages = {1-2},
booktitle = {2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits), Kyoto, Japan, June 11-16, 2023},
publisher = {IEEE},
isbn = {978-4-86348-806-9},
}