2020
DOI: 10.1088/1361-6404/ab7027
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Combining high-performance hardware, cloud computing, and deep learning frameworks to accelerate physical simulations: probing the Hopfield network

Abstract: The synthesis of high-performance computing (particularly graphics processing units), cloud computing services (like Google Colab), and high-level deep learning frameworks (such as PyTorch) has powered the burgeoning field of artificial intelligence. While these technologies are popular in the computer science discipline, the physics community is less aware of how such innovations, freely available online, can improve research and education. In this tutorial, we take the Hopfield network as an example to show … Show more

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Cited by 4 publications
(2 citation statements)
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References 26 publications
(46 reference statements)
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“…The research results of Vavilala [16] demonstrate the synthesis of high-performance computations, cloud computational servers (e.g., Google Colab), and high-performance platforms for deep learning (e.g., PyTorch). Using the Hopfield network as an example, the author demonstrates the capabilities of computer modeling for physical experiments and faster learning due to computer modeling of physical processes.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The research results of Vavilala [16] demonstrate the synthesis of high-performance computations, cloud computational servers (e.g., Google Colab), and high-performance platforms for deep learning (e.g., PyTorch). Using the Hopfield network as an example, the author demonstrates the capabilities of computer modeling for physical experiments and faster learning due to computer modeling of physical processes.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The ideas to provide such resources via Internet were elaborated in the past [1]. Now a convenient cloud computing services appeared, and they are considered as computational resources along with the other possibilities [2]. Cloud computing is applied for various resource-consuming calculations, such as overlay analysis [3] or motion capture [4].…”
Section: Introductionmentioning
confidence: 99%