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Verilog Generator of Neural Net Digit Detector for FPGA

It's the project which train neural net to detect dark digits on light background. Then neural net converted to verilog HDL representation using several techniques to reduce needed resources on FPGA and increase speed of processing. Code is production ready to use in real device. It can be easily extended to be used with detection of other objects with different neural net structure.

Requirements

Python 3.5, Tensorflow 1.4.0, Keras 2.1.3

How to run:

  • python r01_train_neural_net_and_prepare_initial_weights.py
  • python r02_rescale_weights_to_use_fixed_point_representation.py
  • python r03_find_optimal_bit_for_weights.py
  • python r04_verilog_generator_grayscale_file.py
  • python r05_verilog_generator_neural_net_structure.py

Verilog already added in repository in ''verilog'' folder. It has everything you need including all code to interact with camera or screen. Neural net verilog description is located in ''verliog/code/neuroset'' folder.

Neural net structure

Neural Net Structure

Device

To recreate the device you need 3 components:

Connection of components

Connection scheme

Connection photo

Demo video with detection

Convolutional Neural Net implementation in FPGA (Demo)

Notes

  • You can change constant num_conv = 2 in r05_verilog_generator_neural_net_structure.py to 1, 2 or 4 convolutional blocks which will work in parallel. More blocks will require more LE in FPGA, but increase the overall speed.

  • Comparison table for different bit weights and number of convolution blocks below (red rows: unable to synthesize, due to Cyclone IV limitations).

Used FPGA resources