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FeedforwardNet is a toolkit that simplifies the development and deployment of deep learning systems for FPGA based devices, either embedded or resource rich. The toolkit enables fast mapping pre-trained network models or applications on FPGA platforms and accelerate with pure logical hardware.

This toolkit contains a bottom up designed inference network library together with the pre-trained CAFFE model converter and network construction flow. This version of code is still under active development. Future version of well integrated toolkit with a web based GUI will be released soon.

  1. caffe converter -- converting caffemodels to weight, mean, val and network parameter files.
  2. codeGenerator -- generating the targeted network model in C++.
  3. example -- several example caffemodels and generated projects.
  4. fpga_cnn -- original accelerator template lib.
  5. scripts -- scripts used to organize the generation flow.
  6. stb_image -- stb image lib with non-preinstallation required.

Getting Started

  1. compile caffe_converver and convert the caffemodel file:
  • cd to caffe_converter.
  • run ./run.sh to compile the converter codes.
  • use ./caffe_converter targeted.prototxt targeted.caffemodel [targeted.binarymodel] to convert the input caffemodel. The detailed explanation and instructions are located in caffe_converter.
  1. generate the targeted network with the net_config_params.txt from caffe_converter:
  • navigate to codeGenerator folder.
  • cp ../caffe_converter/net_config_params.txt ./
  • run ./run_generator.sh and follow the instruction with correct paths,file names and data types. Make sure the test_demo folder in example/ is deleted before starting this step. The detailed explanation and instructions are located in codeGenerator.
  1. test the generated network in C++:
  • navigate to FeedforwardNet/example/, the newly generated network is located here in test_demo/.
  • compile the C++ code with command make.
  • run ./ff_test to test the correctness.
  1. FPGA synthesis and implementation:
  • uncomment HLS_MODE in config.h located in inference_net/.
  • navigate into hls_impl/ folder.
  • run ./syn.sh to start synthesis and generate the inference_net IP.

Help and Support

If you have any suggestions and questions, please contact [email protected].

Bibtex:

@misc{feedforward2016,

author = {Yao Chen, Yang Yu, Chunrong Zhong},

title = {{FeedforwardNet: Enabling Efficient Convolutional Neural Network Application Design on Embedded FPGAs, Version 0.1}},

year = {2016},

url = {https://github.com/microideax/FeedforwardNet} }

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