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
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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 +4 more
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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
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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 +2 more
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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 +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 +9 more
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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 +2 more
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In-memory mechanical computing
Nature Communications, 2023 Mechanical computing requires matter to adapt behavior according to retained knowledge, often through integrated sensing, actuation, and control of deformation.
Tie Mei, Chang Qing Chen
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Graphene Oxide-Based Memristive Logic-in-Memory Circuit Enabling Normally-Off Computing
Nanomaterials, 2023 Memristive logic-in-memory circuits can provide energy- and cost-efficient computing, which is essential for artificial intelligence-based applications in the coming Internet-of-things era.
Yeongkwon Kim +2 more
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