Results 21 to 30 of about 931,753 (278)
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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Memory devices and applications for in-memory computing [PDF]
Nature Nanotechnology, 2020 Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one
Abu Sebastian +3 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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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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Memory-Efficient Fixpoint Computation [PDF]
Formal Methods in System Design, 2020 Abstract Practical adoption of static analysis often requires trading precision for performance. This paper focuses on improving the memory efficiency of abstract interpretation without sacrificing precision or time efficiency. Computationally, abstract interpretation reduces the problem of inferring program invariants to computing a fixpoint
Sung Kook Kim +2 more
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Architecture of Computing System based on Chiplet
Micromachines, 2022 Computing systems are widely used in medical diagnosis, climate prediction, autonomous vehicles, etc. As the key part of electronics, the performance of computing systems is crucial in the intellectualization of the equipment.
Guangbao Shan +5 more
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An Efficient and Robust Partial Differential Equation Solver by Flash-Based Computing in Memory
Micromachines, 2023 Flash memory-based computing-in-memory (CIM) architectures have gained popularity due to their remarkable performance in various computation tasks of data processing, including machine learning, neuron networks, and scientific calculations. Especially in
Yueran Qi +10 more
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Truss Decomposition in Massive Networks [PDF]
, 2012 The k-truss is a type of cohesive subgraphs proposed recently for the study of networks. While the problem of computing most cohesive subgraphs is NP-hard, there exists a polynomial time algorithm for computing k-truss. Compared with k-core which is also
Cheng, James, Wang, Jia
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Mapping Computation with No Memory [PDF]
, 2009 We investigate the computation of mappings from a set $S^n$ to itself with ''in situ programs'', that is using no extra variables than the input, and performing modifications of one component at a time. We consider several types of mappings and obtain effective computation and decomposition methods, together with upper bounds on the program length ...
Burckel, Serge +2 more
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