Implementation of Neural Networks in FPGA
Online Publishing @ NISCAIR
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Authentication Code |
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Title Statement |
Implementation of Neural Networks in FPGA |
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Added Entry - Uncontrolled Name |
B, Jayanthi ; Department of Electronics and Communication Engineering, National Institute of Technology Puducherry, Karaikal 609 609, India |
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Uncontrolled Index Term |
Artificial intelligence, Artificial neural network, Convolutional neural network |
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Summary, etc. |
Artificial Intelligence (AI) refers to the recreation of human intelligence in machines that have been designed to think like humans and mimic their actions. AI has been used in many fields such as image processing, health care, education, and marketing. Machine Learning (ML) has been the sub-division of AI, and deep learning has been the subdivision of ML. Artificial Neural Network has been the most predominantly used deep learning technique. While implementing the ANN technique, knowing whether the implementation could have been done in hardware or software becomes necessary, which is essential to achieve the expected performance. This paper gives a survey on the available methods in which the ANN architecture has been implemented to achieve efficient output with minimal resources. It is vital to study and analyze various strategies for implementation and their functionality. This paper has also explained the advantages and disadvantages of different implementation techniques that allow selecting the most appropriate hardware and respective methodology for optimizing the hardware |
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Publication, Distribution, Etc. |
Indian Journal of Radio & Space Physics (IJRSP) 2022-04-27 12:56:22 |
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Electronic Location and Access |
application/pdf http://op.niscair.res.in/index.php/IJRSP/article/view/62095 |
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Data Source Entry |
Indian Journal of Radio & Space Physics (IJRSP); ##issue.vol## 50, ##issue.no## 2 (2021): IJRSP-JUNE 2021 |
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Language Note |
en |
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Terms Governing Use and Reproduction Note |
Except where otherwise noted, the Articles on this site are licensed underCreative Commons License: CC Attribution-Noncommercial-No Derivative Works 2.5 India© 2012. The Council of Scientific & Industrial Research, New Delhi. |
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