Research group on Embedded Architecture for Multisensing

Haddoc2: Hardware Automated Data-flow Deployment Of CNNs

Haddoc2 is a tool to automatically design FPGA-based hardware accelerators for Convolutional Neural Networks (CNNs). Using a Caffe model, Haddoc2 generates a hardware description of the network (in VHDL-2008) which is constructor and device independent. Haddoc2 is built upon the principals of data-flow stream-based processing of data, and, implements CNNs using a Direct Hardware Mapping approach, where all the actors involved in the CNN processing are physically mapped on the FPGA.

More implementation details can be found in this technical report and the this paper. If you find Haddoc2 useful in your research, please consider citing the following paper

    author = {Abdelouahab, Kamel and Pelcat, Maxime and Serot, Jocelyn. and Bourrasset, Cedric and Berry, Fran{\c{c}}ois},
    doi = {10.1109/LES.2017.2743247},
    issn = {19430663},
    journal = {IEEE Embedded Systems Letters},
    keywords = {CNN,Dataflow,FPGA,VHDL},
    pages = {1--4},
    title = {Tactics to Directly Map CNN graphs on Embedded FPGAs},
    url = {http://ieeexplore.ieee.org/document/8015156/},
    year = {2017}}


haddoc2 can be downloaded at: https://github.com/DreamIP/haddoc2


  • Caffe with a simple CPU-only build is needed.
  • Quartus II or Vivado (Optional) : to compile and synthesize your design
  • GPStudio FPGA (Optional): Haddoc2 generated accelerators are compatible with GPStudio, a tool-chain to to deploy image processing applications on FPGA-based smart cameras.


To run haddoc2, binders are available in the bin/ directory.

python haddoc2.py \
      --proto=<path to caffe prototxt> \
      --model=<path to caffe model> \
      --out=<output directory> \
      --nbits=<fixed point format. Default nbits is 8>


Note that Haddoc2 needs to know where your Caffe and Haddoc2 installation directories are. Please add the following environment variables or edit you .bashrc file in Linux. For instance:

export CAFFE_ROOT="$HOME/caffe/
export HADDOC2_ROOT="$HOME/dev/haddoc2"

Components required to implement the supported CNN layers can be found at lib/hdl/ directory.
Important: Be sure to synthesize your project in VHDL 2008 mode

Generating an example

example/ directory contains a pre-trained BVLC_caffe model version of the Lenet5 CNN. Please use the Makefile given to test Haddoc2.

  • make hdl generates the VHDL description of the CNN
  • make quartus_proj creates a simple Quartus II project to implement LeNet on an Intel Cyclone V FPGA
  • make compile lunches Quartus tool to compile and synthesize your design. This command requires quartus binary to be on your path

make hdl
    Haddoc2 CNN parameter parser:
    prototxt: ./caffe/lenet.prototxt
    caffe model: ./caffe/lenet.caffemodel
    vhdl out: ./hdl_generated
    bit width : 5
Generated toplevel file: ./hdl_generated/cnn_process.vhd

make quartus_proj
    Succefully generated quartus project

make compile
    quartus_map cnn_process -c cnn_process