Zobrazeno 91 - 100
of 867
pro vyhledávání: '"Huazhong Yang"'
Autor:
Zixuan Zhou, Xuefei Ning, Yi Cai, Jiashu Han, Yiping Deng, Yuhan Dong, Huazhong Yang, Yu Wang
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200434
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::817f49ce9417f625bb996dcc30e5ac1d
https://doi.org/10.1007/978-3-031-20044-1_33
https://doi.org/10.1007/978-3-031-20044-1_33
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198410
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2aa1ceb55bda54725cbd22faaef7782e
https://doi.org/10.1007/978-3-031-19842-7_29
https://doi.org/10.1007/978-3-031-19842-7_29
Autor:
Jialong Liu, Chen Sun, Wenjun Tang, Zijie Zheng, Yongpan Liu, Huazhong Yang, Chen Jiang, Kai Ni, Xiao Gong, Xueqing Li
Publikováno v:
2021 IEEE International Electron Devices Meeting (IEDM).
Publikováno v:
DAC
Neural networks running on cloud servers have been widely used in smart surveillance, but they require high bandwidth to upload videos. Edge-cloud collaborative encoding based on ROI (Region-Of-Interest) can reduce bandwidth requirement, but it suffe
Publikováno v:
Neurocomputing. 399:296-306
This work focuses on combining nonparametric topic models with Auto-Encoding Variational Bayes (AEVB). Specifically, we first propose iTM-VAE, where the topics are treated as trainable parameters and the document-specific topic proportions are obtain
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 39:1414-1427
The emerging resistive random-access memory (RRAM) has been widely applied in accelerating the computing of deep neural networks. However, it is challenging to achieve high-precision computations based on RRAM due to the limits of the resistance leve
Autor:
Huazhong Yang, Jinshan Yue, Zhe Yuan, Yixiong Yang, Xiaoyu Feng, Xueqing Li, Yongpan Liu, Jian Zhao, Jingyu Wang
Publikováno v:
IEEE Journal of Solid-State Circuits. 55:465-477
STICKER is an energy-efficient convolutional neural network (NN) processor. It mainly improves energy efficiency by making full use of sparsity. The network sparsity can potentially lower storage and computation requirements. However, the sparsity di
Autor:
Huifeng Zhu, Fei Qiao, Zhe Chen, Li Luo, Qi Wei, Erxiang Ren, Zheyu Liu, Xin-Jun Liu, Xuan Zhang, Huazhong Yang, Kaige Jia
Publikováno v:
IEEE Transactions on Circuits and Systems I: Regular Papers. 67:389-400
In the era of Artificial Intelligence (AI), bio-inspired perceptual computing system design brings favorable opportunities, while still facing considerable challenges in the meantime. Especially for tasks of image recognition in power-limited vision-
Publikováno v:
IEEE Access, Vol 8, Pp 48720-48730 (2020)
Feature extraction is an essential part of automatic speech recognition (ASR) to compress raw speech data and enhance features, where conventional implementation methods based on the digital domain have encountered energy consumption and processing s
Autor:
Yinan Sun, Zichen Fan, Xin-Jun Liu, Fei Qiao, Shuzheng Xu, Huazhong Yang, Zheyu Liu, Zheng Qu, Qi Wei
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 28:198-211
The scale-invariant feature transform (SIFT) algorithm is still one of the most reliable image feature extraction methods. Despite its excellent robustness on various image transformations, SIFT’s intensive computational burden has been severely pr