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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Simplified Multiplication in Convolutional Neural Networks
 Juhaňák, Pavel ; Jaroš, Jiří (referee) ; Sekanina, Lukáš (advisor)
This thesis provides an introduction to classical and convolutional neural networks. It describes how hardware multiplication is conventionally performed and optimized. A simplified multiplication method is proposed, namely multiplierless multiplication. This method is implemented and integrated into the TypeCNN library. The cost of the hardware solution of both conventional and simplified multipliers is estimated. The thesis also introduces software tools developed to work with convolutional neural networks and datasets used to test them in the image classification task. Test architectures and experimentation methodology are proposed. The results are evaluated, and both the classification accuracy and cost of the hardware solution are discussed.
Detailed record
Simplified Multiplication in Convolutional Neural Networks
 Juhaňák, Pavel ; Jaroš, Jiří (referee) ; Sekanina, Lukáš (advisor)
This thesis provides an introduction to classical and convolutional neural networks. It describes how hardware multiplication is conventionally performed and optimized. A simplified multiplication method is proposed, namely multiplierless multiplication. This method is implemented and integrated into the TypeCNN library. The cost of the hardware solution of both conventional and simplified multipliers is estimated. The thesis also introduces software tools developed to work with convolutional neural networks and datasets used to test them in the image classification task. Test architectures and experimentation methodology are proposed. The results are evaluated, and both the classification accuracy and cost of the hardware solution are discussed.
Detailed record

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