Livoa LogoLivoa
Input


32 x 32 x 1

Convolution (5x5)


Feature Map

28 x 28 x 6

Subsampling
Convolution (5x5)


Feature Map

14 x 14 x 6

Subsampling
Convolution (5x5)


Feature Map

10 x 10 x 16

Feature Map


5 x 5 x 16

Fully connected


120

Fully connected


84

Output


10

Dataset


Raw texture images for training, validation, testing
Software Implementation


- LeNet model (Python + NumPy)


- Training & testing


- Store results
Hardware Implementation


- Convert model (Python to C++)


- Vivado HLS: C++ to RTL


- Vivado HLx: synthesis & bitstream


- Spartan-7 FPGA
Performance Comparison


• Accuracy (CPU vs FPGA)


• Training Time (CPU vs FPGA)


• Hardware Acceleration Validation

cnn

by vaish

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