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
计算技术研究所 [3]
长春光学精密机械与物... [1]
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SLAM-CIM: A Visual SLAM Backend Processor With Dynamic-Range-Driven-Skipping Linear-Solving FP-CIM Macros
期刊论文
OAI收割
IEEE JOURNAL OF SOLID-STATE CIRCUITS, 2024, 页码: 13
作者:
Li, Mengjie
;
Zhu, Haozhe
;
He, Siqi
;
Zhang, Hongyi
;
Liao, Jie
|
收藏
|
浏览/下载:10/0
|
提交时间:2024/12/06
Simultaneous localization and mapping
Sorting
In-memory computing
Visualization
Energy efficiency
Optimization
Common Information Model (computing)
Compute in memory (CIM)
floating point (FP)
linear system solver
simultaneous localization and mapping (SLAM)
Enabling Secure NVM-Based in-Memory Neural Network Computing by Sparse Fast Gradient Encryption
期刊论文
OAI收割
IEEE TRANSACTIONS ON COMPUTERS, 2020, 卷号: 69, 期号: 11, 页码: 1596-1610
作者:
Cai, Yi
;
Chen, Xiaoming
;
Tian, Lu
;
Wang, Yu
;
Yang, Huazhong
|
收藏
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浏览/下载:55/0
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提交时间:2020/12/10
Artificial neural networks
Nonvolatile memory
Encryption
Computational modeling
Hardware
Non-volatile memory (NVM)
compute-in-memory (CIM)
neural network
security
encryption
The Impact of Ferroelectric FETs on Digital and Analog Circuits and Architectures
期刊论文
OAI收割
IEEE DESIGN & TEST, 2020, 卷号: 37, 期号: 1, 页码: 79-99
作者:
Chen, Xiaoming
;
Sun, Xiaoyu
;
Wang, Panni
;
Datta, Suman
;
Hu, Xiaobo Sharon
|
收藏
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浏览/下载:30/0
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提交时间:2020/12/10
Iron
Transistors
Computer architecture
Switches
Capacitance
Logic gates
Computational modeling
Ferroelectric Field Effect Transistor
FeFET
Negative Capacitance Field Effect Transistor
NCFET
Preisach model
FPGAs
content addressable memories
CAM
TCAM
compute-in-memory
analog synapse
Image parallel processing based on GPU (EI CONFERENCE)
会议论文
OAI收割
2010 IEEE International Conference on Advanced Computer Control, ICACC 2010, March 27, 2010 - March 29, 2010, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States
作者:
Wang J.-L.
;
Wang J.-L.
收藏
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浏览/下载:31/0
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提交时间:2013/03/25
In order to solve the compute-intensive character of image processing
based on advantages of GPU parallel operation
parallel acceleration processing technique is proposed for image. First
efficient architecture of GPU is introduced that improves computational efficiency
comparing with CPU. Then
Sobel edge detector and homomorphic filtering
two representative image processing algorithms
are embedded into GPU to validate the technique. Finally
tested image data of different resolutions are used on CPU and GPU hardware platform to compare computational efficiency of GPU and CPU. Experimental results indicate that if data transfer time
between host memory and device memory
is taken into account
speed of the two algorithms implemented on GPU can be improved approximately 25 times and 49 times as fast as CPU
respectively
and GPU is practical for image processing. 2010 IEEE.
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