Application of the residue number system to reduce hardware costs of the convolutional neural network implementation
| Autor: | Georgii Valuev, Maria V. Valueva, Nikolay I. Chervyakov, Nikolay Nagornov, Pavel A. Lyakhov |
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| Rok vydání: | 2020 |
| Předmět: |
Numerical Analysis
General Computer Science Artificial neural network business.industry Computer science Applied Mathematics Process (computing) 010103 numerical & computational mathematics 02 engineering and technology Residue number system 01 natural sciences Convolutional neural network Theoretical Computer Science Software Modeling and Simulation Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0101 mathematics business MATLAB computer Implementation Computer hardware computer.programming_language |
| Zdroj: | Mathematics and Computers in Simulation. 177:232-243 |
| ISSN: | 0378-4754 |
| DOI: | 10.1016/j.matcom.2020.04.031 |
| Popis: | Convolutional neural networks are a promising tool for solving the problem of pattern recognition. Most well-known convolutional neural networks implementations require a significant amount of memory to store weights in the process of learning and working. We propose a convolutional neural network architecture in which the neural network is divided into hardware and software parts to increase performance and reduce the cost of implementation resources. We also propose to use the residue number system (RNS) in the hardware part to implement the convolutional layer of the neural network. Software simulations using Matlab 2018b showed that convolutional neural network with a minimum number of layers can be quickly and successfully trained. The hardware implementation of the convolution layer shows that the use of RNS allows to reduce the hardware costs on 7.86%–37.78% compared to the two’s complement implementation. The use of the proposed heterogeneous implementation reduces the average time of image recognition by 41.17%. |
| Databáze: | OpenAIRE |
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