FPGA parallel acceleration method based on convolution neural network (CNN)
A convolutional neural network and network technology, applied in the field of FPGA parallel acceleration of convolutional neural networks, can solve the problems of not fully exerting the FPGA computing potential and poor scalability.
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[0012] Below, in conjunction with accompanying drawing, the present invention is described in detail as follows:
[0013] The FPGA parallel acceleration method of a convolutional neural network of the present invention comprises the following points:
[0014] One is the CNN model structure. The CNN model structure adopted in the present invention is made up of 1 input layer input, 1 output layer output, 2 convolution layers, 2 pooling and a fully connected network Softmax, such as figure 1 shown. In this experiment, the input image set is the handwritten digital image set MNIST. The size of each image is 28×28 pixels. The specific network structure is as follows:
[0015] Input layer: 28×28;
[0016] C1Conv layer: 3kernels, each with size 5×5, stride=1;
[0017] S1Max-pooling layer: each with size 2×2, stride=2, β=1.0 b=0.0;
[0018] C2Conv layer: 6kernels, each with size 5×5, stride=1;
[0019] S2Max-pooling layer: each with size 2×2, stride=2, β=1.0 b=0.0;
[0020] ...
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