- This repository contains all the necessary files for the deployment of the deep neural networks like GoogleNet [1] and ResNet-50 [2] on on multiple Intel Stratix 10 FPGAs using an custom built FPGA plugin.
- Using a combination of two machine learning frameworks - TVM and Intel OpenVINO, CNNs are tested on the ImageNet dataset.
- TVM (Tensor Virtual Machine) is used for OpenCL code generation of the CNN topologies.
- The OpenVINO Computer Vision Toolkit is used as an inference engine for running the generated code on FPGAs.
CustoNN2
--- OpenCl_Kernels //OpenCl Kernels for GoogleNet and ResNet-50.
--- Googlenet // GoogleNet Kernels.
--- baseline_architecture // Baseline Kernels without any optimization.
--- global_optimized // Increased DSP usage and global memory optimizations with the help of loop unrolling etc.
--- hybrid // Data transfer optimized with the help of internal channels, I/O channels and global memory.
--- Resnet_50 // ResNet-50 Kernels.
--- baseline // Baseline Kernels without any optimization.
--- optimized_v1 // Global memory optimizations with the help of loop unrolling and loop pipelining.
--- units_opv1 // Divind into 16 units of the model and global memory optimizations with the help of loop unrolling etc.
--- dldt // Sub-directory for FPGA Plugin. dldt - Deep Learning Deployment Toolkit.
--- inference_engine
--- src
--- noctua_plugin
--- fpga_plugin.cpp //FPGA Plugin in C++ to deploy neural networks.
The READMEs in the sub-directories give extensive information on the kernels and the steps needed to execute the FPGA Plugin.
[1] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich: Going Deeper with Convolutions.
[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Deep Residual Learning for Image Recognition.
The repository is licensed under Apache 2.0 License.