Backpropagation Algorithm and its Hardware Implementations: A Review

Nowadays, artificial intelligence is gearing up at faster pace. Hardware integration with Artificial Neural Networks (ANN) have paved the way to different applications in areas like control engineering, robotics and navigation. There has been extensive research in the field of machine learning to us...

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Veröffentlicht in:Journal of physics. Conference series Jg. 1804; H. 1; S. 12169
Hauptverfasser: Kuninti, Shivani, Rooban, S
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.02.2021
Schlagworte:
Algorithms
Artificial intelligence
Artificial neural networks
Back propagation
Back propagation networks
Field programmable gate arrays
Hardware
Learning theory
Machine learning
Memristors
Microcontrollers
Neural networks
Parallel processing
Parameter identification
Physics
Propagation
Robotics
ISSN:1742-6588, 1742-6596
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Abstract Nowadays, artificial intelligence is gearing up at faster pace. Hardware integration with Artificial Neural Networks (ANN) have paved the way to different applications in areas like control engineering, robotics and navigation. There has been extensive research in the field of machine learning to use the system in advanced applications. The combination of hardware and software modules help to build systems in a robust manner. This paper focuses mainly on back propagation algorithm and its different hardware implementation using FPGA, ASIC, Memristor and Microcontroller. Implementation of back propagation algorithm on Field programmable gate arrays is analysed in detail. FPGAs parallel processing feature makes it unique for neural networks. The parallel processing models implement back propagation algorithms for calculating errors in the hidden layers of neural networks. This review helps the researchers to understand the implementation of various hardware with back propagation algorithm also the comparative analysis on the parameters helps to identify the suitable hardware based on their requirements.
AbstractList Nowadays, artificial intelligence is gearing up at faster pace. Hardware integration with Artificial Neural Networks (ANN) have paved the way to different applications in areas like control engineering, robotics and navigation. There has been extensive research in the field of machine learning to use the system in advanced applications. The combination of hardware and software modules help to build systems in a robust manner. This paper focuses mainly on back propagation algorithm and its different hardware implementation using FPGA, ASIC, Memristor and Microcontroller. Implementation of back propagation algorithm on Field programmable gate arrays is analysed in detail. FPGAs parallel processing feature makes it unique for neural networks. The parallel processing models implement back propagation algorithms for calculating errors in the hidden layers of neural networks. This review helps the researchers to understand the implementation of various hardware with back propagation algorithm also the comparative analysis on the parameters helps to identify the suitable hardware based on their requirements.
Author Rooban, S
Kuninti, Shivani
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Cites_doi 10.1109/TCSI.2017.2729787
10.1109/TCAD.2006.884574
10.1109/JPROC.2011.2166749
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SubjectTerms Algorithms
Artificial intelligence
Artificial neural networks
Back propagation
Back propagation networks
Field programmable gate arrays
Hardware
Learning theory
Machine learning
Memristors
Microcontrollers
Neural networks
Parallel processing
Parameter identification
Physics
Propagation
Robotics
Title Backpropagation Algorithm and its Hardware Implementations: A Review
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