DnnWeaver v2.0 is an open-source framework for accelerating Deep Neural Networks (DNNs) on FPGAs.
If you use this work, please cite our paper published in The 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 2016.
H. Sharma, J. Park, D. Mahajan, E. Amaro, J. K. Kim, C. Shao, A. Mishra, H. Esmaeilzadeh, "From High-Level Deep Neural Models to FPGAs", in the Proceedings of the 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 2016.
Python dependencies:
pip install -r requirements.txt
Vivado Tool version:
Vivado 2018.2
dnnweaver2-tutorial.ipynb provides a tutorial on how to use the tool
Dependencies:
darkflow (https://github.com/thtrieu/darkflow)
OpenCV (cv2)
Here's a sample project that uses DnnWeaver v2.0 to perform real-time image recognition with a drone https://github.com/ardorem/dnnweaver2.drone
Copyright 2018 Hadi Esmaeilzadeh
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Hardik Sharma (hsharma@gatech.edu)