Cost-Performance Comparison of Various Accelerator Implementation Platforms for Deep Convolutional Neural Network
Due to the high accuracy, DCNN is a popular deep learning approach for object recognition and classification. But, the computing complexity of DCNN is too high for real-time application. Therefore, many acceleration methods like GPU and FPGAs are developed and competing with each other. The purpose of this paper is to assess the pros and the cons of many acceleration methods including GPGPU and FPGA-based approaches like Xilinx SDSoC, and Xilinx SDAccel. We will consider the installation cost (board price) as well as the operation cost (the energy consumption) and the speed of each acceleration method in the analysis.
KeywordsReconfigurable high-performance computing Heterogeneous computing systems Intelligent computing and neural networks
This work was supported by National program for Excellence in software at Handong Global University (2017-0-00130) funded by Ministry of Science and ICT in Korea.
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