DeepDive
Algorithm/Architecture Co-Design for FPGA-Based DNN Acceleration
DeepDive is an integrative algorithm/architecture co-design framework for deep separable convolutional neural networks on FPGAs.
Key Features
- Fully functional framework for agile power-efficient execution of DSCNNs
- Synthesis targeting Xilinx ZCU102 FPGA
- MobileNetV2 acceleration with up to 8.6x FPS/W improvement
- Model optimization: quantization (4-bit), layer fusion, pruning, activation approximation
- Hardware optimization: pipelining, window buffering
Publication
M. Baharani, U. Sunil, K. Manohar, S. Furgurson, and H. Tabkhi, “DeepDive: An Integrative Algorithm/Architecture Co-Design for Deep Separable Convolutional Neural Networks,” GLSVLSI 2021.
| Paper | Code |