PyTorch is the fastest growning training framework in AI and deep neural networks (DNN).
ONNX is the industry open standard for saving, distributing, and deploying AI and DNN solutions in an interoperatable way.
TensorFlow remains fully supported and now leverages the ONNX standard.
Coupling PyTorch, TensorFlow, and ONNX support creates a training-to-deployment workflow encompassing all of the leading industry toolboxes. Deploying DNNs for edge-compute radio frequency systems has never been easier.
Training to Deployment Workflow
The figure above outlines the workflow for training, optimizing, and deploying a neural network on the AIR-T. All python packages and dependencies are included on the AirStack 0.3.0+, which is the API for the AIR-T.
Step 1: Train
To simplify the process we provide an example neural network that performs a simple mathematical calculation instead of being trained on data. This toolbox provides all of the necessary code, examples, and benchmarking tools to guide the user in the training to deployment workflow. The process will be the exact same for any other trained neural network.
Step 2: Optimize
Optimize the neural network model using NVIDIA's TensorRT. The output of this step is a file containing the optimized network for deployment on the AIR-T.
Step 3: Deploy
The final step is to deploy the optimized neural network on the AIR-T for inference. This toolbox accomplishes this task by leveraging the GPU/CPU shared memory interface on the AIR-T to receive samples from the receiver and feed the neural network using Zero Copy, i.e., no device-to-host or host-to device copies are performed. This maximizes the data rate while minimizing the latency.