cuda_cnn

This is a convolutional neural network implemented using C and CUDA.The model is deployed on embedded devices.

Layer configuration

• Convolution

Input: 28 * 28 * 1 Output: 24 * 24 * 6 Kernel size: 5 * 5 Kernel amount: 6 Stride: 1

• Tanh

• Pooling

Input: 24 * 24 * 6 Output: 12 * 12 * 6 Window size: 2 * 2 Stride: 2

• FullyConnected1

Input: 12 * 12 * 6 Output: 45

• Sigmoid

• FullyConnected2

Input: 45 Output: 10

• Sigmoid

Dataset

• MNIST: 60k train set, 10k test set
• 28 * 28 * 1

Accuracy

• 20 epoch: 98.71%

Experiment environment

Development environment: HP Pavilion Gaming Notebook

• CPU: Intel Core i7-6700HQ @ 2.60Ghz
• GPU: NVIDIA GeForce GTX 950M

Production environment: NVIDIA Jetson Nano

• CPU: Quad-core ARM A57 @ 1.43 GHz
• GPU: 128-core Maxwell

Usage

• Generating model

Do the following in a development environment.

make
./mnist CPU
./mnist GPU

• Deployment model

The following operations are performed in the production environment.

sudo cp ./params.txt /usr/src/tensorrt/data/mnist
sudo mkdir /usr/src/tensorrt/samples/cuda_cnn
cd Jetson_Nano
sudo cp ./cuda_cnn.cpp /usr/src/tensorrt/samples/cuda_cnn
sudo cp ./Makefile /usr/src/tensorrt/samples/cuda_cnn
cd /usr/src/tensorrt/samples/cuda_cnn
sudo make
cd /usr/src/tensorrt/bin
./cuda_cnn

Focus on the frame, press the P key for prediction analysis, press the Q key to exit the program.