中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
深圳先进技术研究院 [8]
计算技术研究所 [8]
广州生物医药与健康研... [2]
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南海海洋研究所 [1]
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OAI收割 [25]
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期刊论文 [25]
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2023 [1]
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面向高能效加速器的二值化神经网络设计和训练方法
期刊论文
OAI收割
计算机辅助设计与图形学学报, 2023, 卷号: 35, 期号: 6, 页码: 961
作者:
李佳骏
;
许浩博
;
王郁杰
;
肖航
;
王颖
  |  
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2024/05/20
binarized neural networks
deep learning
model training
neural network accelerators
二值化神经网络
深度学习
模型训练
神经网络加速器
Dadu-SV: Accelerate Stereo Vision Processing on NPU
期刊论文
OAI收割
IEEE EMBEDDED SYSTEMS LETTERS, 2022, 卷号: 14, 期号: 4, 页码: 191-194
作者:
Min, Feng
;
Wang, Ying
;
Xu, Haobo
;
Huang, Junpei
;
Wang, Yujie
  |  
收藏
  |  
浏览/下载:53/0
  |  
提交时间:2023/07/12
Hardware acceleration
neural computing
neural processing unit (NPU)
semiglobal matching (SGM)
stereo vision
High peak power, high repetition rate electro-optically Q-switched Nd:GdTaO4 1066 nm laser
期刊论文
OAI收割
INFRARED PHYSICS & TECHNOLOGY, 2022, 卷号: 125
作者:
Zhao, Xilin
;
Li, Xudong
;
Xu, Haobo
;
Yan, Renpeng
;
Wu, Xiaodong
  |  
收藏
  |  
浏览/下载:42/0
  |  
提交时间:2022/12/23
Solid-state lasers
Direct pumping
Q-switched
Nd:GdTaO4
Fast and High-Accuracy Approximate MAC Unit Design for CNN Computing
期刊论文
OAI收割
IEEE EMBEDDED SYSTEMS LETTERS, 2022, 卷号: 14, 期号: 3, 页码: 155-158
作者:
Xiao, Hang
;
Xu, Haobo
;
Chen, Xiaoming
;
Wang, Yujie
;
Han, Yinhe
  |  
收藏
  |  
浏览/下载:30/0
  |  
提交时间:2022/12/07
Approximate computing
convolution neural network
multiply and accumulate (MAC)
Search-Free Inference Acceleration for Sparse Convolutional Neural Networks
期刊论文
OAI收割
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2022, 卷号: 41, 期号: 7, 页码: 2156-2169
作者:
Liu, Bosheng
;
Chen, Xiaoming
;
Han, Yinhe
;
Wu, Jigang
;
Chang, Liang
  |  
收藏
  |  
浏览/下载:26/0
  |  
提交时间:2023/01/10
Internal interconnection
memory bandwidth
sparse accelerators
sparse convolution neural networks (CNNs)
Toward Efficient Computing for Robotics: From a Circuit and System View
期刊论文
OAI收割
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2022, 卷号: 69, 期号: 7, 页码: 3051-3056
作者:
Xu, Haobo
;
Yang, Yuxin
;
Min, Feng
;
Huang, Junpei
;
Chen, Xiaoming
  |  
收藏
  |  
浏览/下载:29/0
  |  
提交时间:2022/12/07
Robots
Kinematics
Planning
Field programmable gate arrays
Image edge detection
Estimation
Circuits and systems
Robotics
accelerator
hardware
kinematics
motion planning
perception
Measurement report: Distinct size dependence and diurnal variation in organic aerosol hygroscopicity, volatility, and cloud condensation nuclei activity at a rural site in the Pearl River Delta (PRD) region, China
期刊论文
OAI收割
ATMOSPHERIC CHEMISTRY AND PHYSICS, 2022, 卷号: 22, 期号: 12, 页码: 8117-8136
作者:
Cai, Mingfu
;
Huang, Shan
;
Liang, Baoling
;
Sun, Qibin
;
Liu, Li
  |  
收藏
  |  
浏览/下载:37/0
  |  
提交时间:2022/11/11
Event-Triggered Cooperative Output Regulation of Heterogeneous Multi-Agent Systems with Adaptive Fault-Tolerant Control
期刊论文
OAI收割
IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, 卷号: 69, 期号: 3, 页码: 1149-1153
作者:
Xu LX(许林兴)
;
Zhao, Lianna
;
Ma HJ(马宏军)
;
Wang YL(王玉龙)
;
Kang HB(康浩博)
  |  
收藏
  |  
浏览/下载:47/0
  |  
提交时间:2021/08/28
Actuators
Automation
Circuit faults
cooperative output regulation
event-triggering mechanism
Fault tolerance
Fault tolerant systems
fault-tolerant control.
Multi-agent systems
Observers
Regulation
LINAC: A Spatially Linear Accelerator for Convolutional Neural Networks
期刊论文
OAI收割
IEEE COMPUTER ARCHITECTURE LETTERS, 2022, 卷号: 21, 期号: 1, 页码: 29-32
作者:
Xiao, Hang
;
Xu, Haobo
;
Wang, Ying
;
Wang, Yujie
;
Han, Yinhe
  |  
收藏
  |  
浏览/下载:48/0
  |  
提交时间:2022/12/07
Linear particle accelerator
Correlation
Kernel
Convolution
Linear regression
System-on-chip
Quantization (signal)
Neural network
acceleration
convolution
linear regression
bit-sparsity
Swallow: A Versatile Accelerator for Sparse Neural Networks
期刊论文
OAI收割
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2020, 卷号: 39, 期号: 12, 页码: 4881-4893
作者:
Liu, Bosheng
;
Chen, Xiaoming
;
Han, Yinhe
;
Xu, Haobo
  |  
收藏
  |  
浏览/下载:47/0
  |  
提交时间:2021/12/01
Accelerator
convolutional (Conv) layers
fully connected (FC) layers
sparse neural networks (SNNs)