FPGA implementation of convolutional neural network based on stochastic computing
| Autor: | Hyeonuk Sim, Hossein Moradian, Daewoo Kim, Jongeun Lee, Mansureh S. Moghaddam, Kiyoung Choi |
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| Rok vydání: | 2017 |
| Předmět: |
010302 applied physics
Stochastic computing Artificial neural network Computer science Binary number 02 engineering and technology Energy consumption 01 natural sciences Convolutional neural network 020202 computer hardware & architecture Computer engineering Encoding (memory) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Field-programmable gate array Efficient energy use |
| Zdroj: | FPT |
| DOI: | 10.1109/fpt.2017.8280162 |
| Popis: | There has been a body of research to use stochastic computing (SC) for the implementation of neural networks, in the hope that it will reduce the area cost and energy consumption. However, no working neural network system based on stochastic computing has been demonstrated to support the viability of SC-based deep neural networks in terms of both recognition accuracy and cost/energy efficiency. In this demonstration we present an SC-based deep nenural network system that is highly accurate and efficient. Our system takes an input image and processes it with a convolutional neural network implemented on an FPGA using stochastic computing to recognize the input image, with nearly the same accuracy as conventional binary implementations. |
| Databáze: | OpenAIRE |
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