Qualcomm Neural Processing SDK | Qualcomm Developer

The Qualcomm® Neural Processing SDK is engineered to help developers save time and effort in optimizing performance of trained neural networks on devices with Qualcomm® AI products. And as part of our Qualcomm® AI Stack, it can help developers deploy AI models fast and run entirely on-device on Qualcomm® AI products.

Our products have extensive AI processing capabilities that are engineered to allow the running of trained neural networks on device without a need for connection to the cloud. The Qualcomm Neural Processing SDK is designed to help developers run one or more neural network models trained in TensorFlow, PyTorch, Keras and ONNX on Qualcomm® platforms, whether that is the CPU, GPU or Qualcomm® Hexagon™ NPU.

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The Qualcomm Neural Processing SDK provides tools for model conversion and execution as well as APIs for targeting the core with the power and performance profile to match the desired user experience. The Qualcomm Neural Processing SDK supports convolutional neural networks, custom layers and more.

The Qualcomm Neural Processing SDK does a lot of the heavy lifting needed to run neural networks on Qualcomm® platforms, which can help provide developers with more time and resources to focus on building new and innovative user experiences.

Meet the SDK:

Android and Linux runtimes for neural network model execution
Acceleration support for Qualcomm® Hexagon™ NPU, Qualcomm® Adreno™ GPUs and Qualcomm® Kryo™, CPUs1
Support for models in TensorFlow, PyTorch, Keras and ONNX formats2
APIs for controlling loading, execution and scheduling on the runtimes
Desktop tools for model conversion
Performance benchmark for bottleneck identification

To make the artificial intelligence developer's life easier, the Qualcomm Neural Processing SDK does not define yet another library of network layers; instead it gives developers the freedom to design and train their networks using familiar frameworks, with TensorFlow, PyTorch, Keras and ONNX being supported at launch. The development workflow is the following:

After designing and training, the model file needs to be converted into a ".dlc" (Deep Learning Container) file to be used by the Snapdragon® Neural Processing Engine (NPE) runtime. The conversion tool will output conversion statistics, including information about unsupported or non-accelerated layers, that the developer can use to adjust the design of the initial model.

Is the Qualcomm Neural Processing SDK Right for You?

Developing for artificial intelligence using the Qualcomm Neural Processing SDK does require a few prerequisites before you can get started creating solutions.

You need to run a convolutional model in one or multiple verticals, including mobile, compute, automotive, IoT, AR, drones, and robotics
You know how to design and train a model or already have a pre-trained model file
Your framework of choice is TensorFlow, PyTorch, Keras and ONNX
You make JAVA APPs for Android or native applications for Android, Windows, or Linux
You have an Ubuntu 20.04 or WSL2 on Windows with an Ubuntu 20.04 development environment
You have a supported device to test your application on