Zobrazeno 1 - 10
of 276
pro vyhledávání: '"Qin Yifan"'
Publikováno v:
Gong-kuang zidonghua, Vol 50, Iss 6, Pp 136-141, 158 (2024)
A precise and fast digital twin mapping method for hydraulic support attitude in fully mechanized working face is proposed to address the problems of low precision, large time delay, and difficulty in balancing precision and time delay in implementin
Externí odkaz:
https://doaj.org/article/2d177b03b15647e697d900a8f7cd9d3f
Autor:
Liu Zhihai, Li Xiang, Cheng Siying, Li Yaru, Jin Wei, Zhang Yu, Qin Yifan, Zhang Yaxun, Li Shanshan, Lotnyk Andriy, Yuan Libo
Publikováno v:
Nanophotonics, Vol 12, Iss 15, Pp 3179-3187 (2023)
The control of information is a defining feature of the information age, and the optical modulator likewise has a crucial role in optical networks. The transmission, processing, and storage of data have demanded low energy consumption and high speed
Externí odkaz:
https://doaj.org/article/fc64175fef2b49e4b41b88baabeaf0db
Publikováno v:
Gong-kuang zidonghua, Vol 49, Iss 9, Pp 122-131, 139 (2023)
The deep integration of the coal industry and artificial intelligence (AI) is an important path for modern mines to achieve intelligent personnel reduction, cost reduction, and efficiency improvement. AI empowerment in the entire process and business
Externí odkaz:
https://doaj.org/article/d3e30a450d7540a9b99137e01292dc86
Autor:
Qin, Yifan, Jia, Zhenge, Yan, Zheyu, Mok, Jay, Yung, Manto, Liu, Yu, Liu, Xuejiao, Wen, Wujie, Liang, Luhong, Cheng, Kwang-Ting Tim, Hu, X. Sharon, Shi, Yiyu
This paper proposes an ultra-low power, mixed-bit-width sparse convolutional neural network (CNN) accelerator to accelerate ventricular arrhythmia (VA) detection. The chip achieves 50% sparsity in a quantized 1D CNN using a sparse processing element
Externí odkaz:
http://arxiv.org/abs/2410.17395
Reconfigurable Intelligent Surfaces (RIS) are programmable metasurfaces utilizing sub-wavelength meta-atoms and a controller for precise electromagnetic wave manipulation. This work introduces an innovative channel coding scheme, termed RIS-based dif
Externí odkaz:
http://arxiv.org/abs/2408.09132
Compute-in-memory (CIM) accelerators using non-volatile memory (NVM) devices offer promising solutions for energy-efficient and low-latency Deep Neural Network (DNN) inference execution. However, practical deployment is often hindered by the challeng
Externí odkaz:
http://arxiv.org/abs/2406.06544
Autor:
Chen, Shuyi, Hui, Yingzhe, Qin, Yifan, Yuan, Yueyi, Meng, Weixiao, Luo, Xuewen, Chen, Hsiao-Hwa
Semantic communication has gained significant attention recently due to its advantages in achieving higher transmission efficiency by focusing on semantic information instead of bit-level information. However, current AI-based semantic communication
Externí odkaz:
http://arxiv.org/abs/2312.00535
Compute-in-Memory (CiM), built upon non-volatile memory (NVM) devices, is promising for accelerating deep neural networks (DNNs) owing to its in-situ data processing capability and superior energy efficiency. Unfortunately, the well-trained model par
Externí odkaz:
http://arxiv.org/abs/2307.15853
Deep Neural Networks (DNNs) have demonstrated impressive performance across a wide range of tasks. However, deploying DNNs on edge devices poses significant challenges due to stringent power and computational budgets. An effective solution to this is
Externí odkaz:
http://arxiv.org/abs/2306.06923
Compute-in-memory (CIM) accelerators built upon non-volatile memory (NVM) devices excel in energy efficiency and latency when performing Deep Neural Network (DNN) inference, thanks to their in-situ data processing capability. However, the stochastic
Externí odkaz:
http://arxiv.org/abs/2305.14561