基于FPGA的卷积神经网络浮点激励函数实现
发布时间：2018-09-06 11:14
【摘要】：卷积神经网络因深度学习概念的提出再一次被研究人员所重视.激励函数是卷积神经网络的一个重要组成部分,选取了sigmoid函数作为实验对象.讨论了当前几种可行的逼近方法,最终采用分段四阶多项式拟合sigmoid函数.在FPGA上使用Verilog硬件描述语言设计了并行电路,并采集了数据集进行FPGA与CPU版本caffe库进行运算效率对比.实验结果表明,此种方法误差小效率高,FPGA在深度学习领域有着广阔的应用前景.
[Abstract]:Convolutional neural networks have once again been paid attention to by researchers because of the concept of deep learning. The excitation function is an important part of the convolution neural network. The sigmoid function is selected as the experimental object. Several feasible approximation methods are discussed. Finally, the piecewise fourth order polynomial is used to fit the sigmoid function. The parallel circuit is designed by using Verilog hardware description language on FPGA, and the data sets are collected and compared with FPGA and CPU version caffe library. The experimental results show that this method has a wide application prospect in the field of deep learning with small error and high efficiency.
【作者单位】： 四川大学计算机学院视觉合成图形图像技术国家重点学科实验室;
【基金】：国家“八六三”计划项目(2015AA016405) 四川省科技厅科技支撑项目(2016GZ0097)
【分类号】：TN791;TP183
本文编号：2226181
[Abstract]:Convolutional neural networks have once again been paid attention to by researchers because of the concept of deep learning. The excitation function is an important part of the convolution neural network. The sigmoid function is selected as the experimental object. Several feasible approximation methods are discussed. Finally, the piecewise fourth order polynomial is used to fit the sigmoid function. The parallel circuit is designed by using Verilog hardware description language on FPGA, and the data sets are collected and compared with FPGA and CPU version caffe library. The experimental results show that this method has a wide application prospect in the field of deep learning with small error and high efficiency.
【作者单位】： 四川大学计算机学院视觉合成图形图像技术国家重点学科实验室;
【基金】：国家“八六三”计划项目(2015AA016405) 四川省科技厅科技支撑项目(2016GZ0097)
【分类号】：TN791;TP183
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