DeepBurning Save

Automatic generation of FPGA-based learning accelerators for the neural network family

Project README

DeepBurning is an end-to-end automatic neural network accelerator design tool for specialized learning tasks. It provides a unified deep learning acceleration solution to high-level application designers without dealing with the model training and hardware accelerator tuning. You can refer to DeepBurning homepage for more details.

DeepBurning mainly includes the following four parts:

  • YOSO:search for the optimized neural network architecture and the NPU configuration

  • Model-zoo:pre-compiled neural network instructions

  • Zynq-prj: Pre-built zynq project on ZC706 and MZ7100.

  • NPU-IP: NPU ip core (netlist) It is a general NPU core that supports almost all the main-stream neural network models. It can be further customized for specific learning tasks and run at higher speed and less resource overhead.

Open Source Agenda is not affiliated with "DeepBurning" Project. README Source: groupsada/DeepBurning
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