Revolutionizing AI Deployment in RF Systems: Deepwave Digital's AIR-T Workflow Toolbox

Deepwave Digital has unveiled a game-changing workflow toolbox designed to streamline the process of creating, training, optimizing, and deploying neural networks on the AIR-T (Artificial Intelligence Radio Transceiver) platform. This innovative toolbox operates natively on both AIR-T and AirStack, eliminating the need for additional software installations and significantly simplifying the deployment of AI-enabled radio frequency (RF) systems.

The new workflow consists of three primary steps: Train, Optimize, and Deploy. This streamlined approach allows developers to take an existing TensorFlow model and deploy it on the AIR-T in less than a minute. The process is designed to work seamlessly with AirStack 0.3.0+ API, ensuring compatibility and ease of use for AIR-T users.

To facilitate the learning process, Deepwave Digital provides an example TensorFlow neural network that performs a simple mathematical calculation. This example serves as a template for users to understand the training process, which can be easily adapted for more complex neural networks trained on real-world data. The toolbox includes all necessary code, examples, and benchmarking tools to guide users through the training phase.

Once the neural network is trained, the next step involves optimization using NVIDIA's TensorRT. This crucial step enhances the network's performance, preparing it for efficient deployment on the AIR-T. The optimization process results in a file containing the optimized network, ready for the final deployment stage.

The final step in the workflow is deploying the optimized neural network on the AIR-T for inference. This toolbox leverages the GPU/CPU shared memory interface on the AIR-T to receive samples from the receiver and feed the neural network using Zero Copy technology. This approach eliminates the need for device-to-host or host-to-device copies, maximizing data rate while minimizing latency.

The simplified AI deployment workflow on AIR-T offers several key advantages: 1. Native compatibility with AIR-T and AirStack 2. Rapid deployment of TensorFlow models 3. Comprehensive toolbox with examples and benchmarking tools 4. Optimized performance through TensorRT integration 5. Efficient use of GPU/CPU shared memory for improved data handling 6. Minimized latency and maximized data rate in RF systems

Deepwave Digital's new AI deployment workflow toolbox represents a significant advancement in the field of AI-enabled radio frequency systems. By simplifying the process from training to deployment, it opens up new possibilities for researchers, developers, and engineers working with AIR-T technology. As the toolbox is open-source and runs natively on all AIR-T models, it paves the way for accelerated innovation and development in RF applications leveraging artificial intelligence.