Results 1 to 10 of about 131,404 (217)

Memory Optimization Method for Parallel Computing Framework Based on Distributed Dataset [PDF]

Jisuanji gongcheng, 2023
With the rapid development of scientific computing and artificial intelligence technology, parallel computing in distributed environment has become an important method for solving large-scale theoretical computing and data processing problems.
XIA Libin, LIU Xiaoyu, JIANG Xiaowei, SUN Gongxing
doaj   +1 more source

Emerging 2D Ferroelectric Devices for In‐Sensor and In‐Memory Computing [PDF]

Advanced Materials
Jianbin Xu
exaly   +2 more sources

Cryogenic In-Memory Computing for Quantum Processors Using Commercial 5-nm FinFETs

IEEE Open Journal of Circuits and Systems, 2023
Cryogenic CMOS circuits that efficiently connect the classical domain with the quantum world are the cornerstone in bringing large-scale quantum processors to reality. The major challenges are, however, the tight power budget (in the order of milliwatts)
Shivendra Singh Parihar, Simon Thomann, Girish Pahwa, Yogesh Singh Chauhan, Hussam Amrouch   +4 more
doaj   +1 more source

Will computing in memory become a new dawn of associative processors?

Memories - Materials, Devices, Circuits and Systems, 2023
Computer architecture faces an enormous challenge in recent years: while the demand for performance is constantly growing, the performance improvement of general-purpose CPU has almost stalled.
Leonid Yavits
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Device Variation Effects on Neural Network Inference Accuracy in Analog In‐Memory Computing Systems

Advanced Intelligent Systems, 2022
In analog in‐memory computing systems based on nonvolatile memories such as resistive random‐access memory (RRAM), neural network models are often trained offline and then the weights are programmed onto memory devices as conductance values.
Qiwen Wang, Yongmo Park, Wei D. Lu
doaj   +1 more source

Progress and Benchmark of Spiking Neuron Devices and Circuits

Advanced Intelligent Systems, 2021
The sustainability of ever more sophisticated artificial intelligence relies on the continual development of highly energy‐efficient and compact computing hardware that mimics the biological neural networks.
Fu-Xiang Liang, I-Ting Wang, Tuo-Hung Hou   +2 more
doaj   +1 more source

Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials [PDF]

Nano-Micro Letters
Yong-Wei Zhang, Hangbo Zhou, Kah-Wee Ang   +2 more
exaly   +2 more sources

Multistate resistive switching behaviors for neuromorphic computing in memristor

Materials Today Advances, 2021
Conventional Von Neumann computing systems encounter increasing challenges in the big-data era due to the constraints by the separated data storage and processing. Resistive random-access memory provides dual functionalities of data storage and computing
B. Sun, S. Ranjan, G. Zhou, T. Guo, Y. Xia, L. Wei, Y.N. Zhou, Y.A. Wu   +7 more
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Floating Gate Transistor‐Based Accurate Digital In‐Memory Computing for Deep Neural Networks

Advanced Intelligent Systems, 2022
To improve the computing speed and energy efficiency of deep neural network (DNN) applications, in‐memory computing with nonvolatile memory (NVM) is proposed to address the time‐consuming and energy‐hungry data shuttling issue.
Runze Han, Peng Huang, Yachen Xiang, Hong Hu, Sheng Lin, Peiyan Dong, Wensheng Shen, Yanzhi Wang, Xiaoyan Liu, Jinfeng Kang   +9 more
doaj   +1 more source

Time Domain Analog Neuromorphic Engine Based on High-Density Non-Volatile Memory in Single-Poly CMOS

IEEE Access, 2022
Increasing the energy efficiency of deep learning systems is critical for improving the cognitive capability of edge devices, often battery operated, as well as for data centers, constrained by the total power envelope.
Tommaso Rizzo, Sebastiano Strangio, Giuseppe Iannaccone   +2 more
doaj   +1 more source