The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Application of the residue number system to reduce hardware costs of the convolutional neural network implementation
Abstract
Abstract 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%.
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Cites
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Recent Advances in Artificial Intelligence Sensors
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“In deep learning, the CNN (or ConvNet) is a class of deep neural network that is the most typically applied in analyzing visual images [20].”
Background
Metadata
- Authors
- Valueva, M. V., Nagornov, N. N., Lyakhov, P. A., Valuev, G. V., & Chervyakov, N. I.
- Venue
- Mathematics and Computers in Simulation
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 177
- Pages
- 232-243
- ISSN
- 0378-4754 (ISSN-L)
- OpenAlex
- W3023192521
- Cited by
- 478
- References
- 24
- FWCI
- 34.95
- Type
- journal article
- Language
- en
- Open access
- closed
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- Valueva, M. V., Nagornov, N. N., Lyakhov, P. A., Valuev, G. V., & Chervyakov, N. I. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation. https://doi.org/10.1016/j.matcom.2020.04.031
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