基于ARM+FPGA平台的二值神经网络加速方法研究

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-01-03 Cooperative journals: 《计算机应用研究》

孙孝辉 宋庆增 金光浩 姜文超

Abstract： At present, the existing convolutional neural network has complicated structure and bases on huge dataset. So it has difficulties in meeting the requirement of computing performance and limitation of energy consumption requested by some practical applications or computing platforms. We studied the binary algorithm based on ARM+ FPGA platform and designed a binary neural network aiming at these applications or platforms. This work reduces the demand for data storage units and simplifies the computational complexity. When implemented in the ARM+ FPGA platform, the convolution multiply-accumulate operation is converted into XNOR logic and popcount operation, which improves the overall operation efficiency and declines the consumption of energy and resources. At the same time, based on the characteristics of data storage in binary neural network, a new row processing algorithm is proposed to improve the throughput of the network. In a word, This implementation is superior to the existing FPGA neural network acceleration methods in terms of GOPS, energy and resource efficiency.

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