Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/58761
Title: Implementation of Neural Networks in FPGA
Authors: B, Jayanthi
Kumar, Lakshmi Sutha
Keywords: Artificial intelligence;Artificial neural network;Convolutional neural network
Issue Date: Jun-2021
Publisher: NIScPR-CSIR, India
Abstract: 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.
Page(s): 57-63
URI: http://nopr.niscair.res.in/handle/123456789/58761
ISSN: 0975-105X (Online); 0367-8393 (Print)
Appears in Collections:IJRSP Vol.50(2) [June 2021]

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