International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 3 May-June 2024 Submit your research before last 3 days of June to publish your research paper in the issue of May-June.

Recurrent Neural Network (RNN) Inference Cloud Computing

Author(s) Deepshikha Saikia, Nihar Pratim Deka, Parusitom Brahma
Country India
Abstract The purpose of this technical article is to investigate the convergence of Recurrent Neural Networks (RNNs) with Cloud Computing considering the potential benefits and challenges that may be encountered in bringing these two systems together. Recurrent Neural Networks (RNNs) constitute a particular type of artificial neural network that is capable of processing data sequentially in such a way that it becomes well-suited for applications that involve handling time series or language processing. On the other hand, cloud computing provides
computer infrastructures on a scalable basis as well as flexible computation services that are accessible whenever they are needed. This paper targets making RNN applications more efficient and faster with the use of RNN libraries on cloud computing platforms through which the cloud services can be used for things like data processing power and storage. By combining RNNs with Cloud Computing, this paper aims to enhance the efficiency and performance of
RNN-based applications by leveraging the cloud's computational power and storage capabilities. The study investigates the impact of deploying RNN inference tasks in the cloud environment, analyzing factors such as latency, cost-effectiveness, and scalability.
Keywords RNN, GenAI, SDN,ASR, QCNN
Field Computer > Data / Information
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-26
Cite This Recurrent Neural Network (RNN) Inference Cloud Computing - Deepshikha Saikia, Nihar Pratim Deka, Parusitom Brahma - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.21440
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