FPGA实现卷积神经网络加速

最新推荐文章于 2025-11-15 19:38:14 发布

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最新推荐文章于 2025-11-15 19:38:14 发布 · 3.1k 阅读

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该博客介绍了如何利用FPGA实现卷积神经网络（CNN）的加速，以处理CIFAR-10数据集的分类任务。详细展示了CIFAR_10.prototxt网络结构，包括卷积、池化、激活和全连接层的流程，并解释了三通道分离的过程。通过在PC上训练模型并提取参数，将模型部署到FPGA中，构建相应的硬件框架。仿真结果显示，在100MHz时钟频率下，FPGA能在约490us内完成一张32x32图像的识别，达到2000fps的帧率，远超CPU性能。

基于Caffe中CIFAR_10.prototxt网络，对CIFAR-10数据集分类。

CIFAR_10.prototxt网络

name: "CIFAR_10"
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
}
layer {
  name: "conv1"   //该层的名称
  type: "Convolution" //该层的类型
  bottom: "data" //该层的输入数据blob
  top: "conv1"   //该层的输出数据blob
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX  //pooling类型，默认值为MAX，也可以设置为AVE，STOCHASTIC
    kernel_size: 2  //pooling核大小，为必设参数
    stride: 2  //pooling核步长，默认值为1（即重叠），但通常设置为2；
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 64
  }
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "ip2"
  top: "prob"
}

卷积、池化、激活、全连接层示意图：

三通道分离：

矩阵卷积演示：

单通道6x6 * 3x3 矩阵算法
// ┌────────

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