Premium tier Qualcomm® Snapdragon™ mobile platforms have extensive heterogeneous computing capabilities that are engineered to allow the running of trained neural networks on device without a need for connection to the cloud. The Qualcomm® Snapdragon™ Neural Processing Engine (NPE) SDK is designed to help developers run one or more neural network models trained in Caffe/Caffe2 or TensorFlow on Snapdragon mobile platforms, whether that is the CPU, GPU or DSP.
The Snapdragon NPE is engineered to help developers save time and effort in optimizing performance of trained neural networks on devices with Snapdragon. It does this by providing 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 Snapdragon NPE supports convolutional neural networks and custom layers.
The Snapdragon NPE does a lot of the heavy lifting needed to run neural networks on Snapdragon mobile platforms, which can help provide developers with more time and resources to focus on building new and innovative user experiences.
If you would like a more in-depth introduction to artificial intelligence and the Neural Processing SDK, we encourage you to view our Snapdragon and Artificial Intelligence at the Edge webinar, which provides an overview of what we have to offer.
Support for models in Caffe, Caffe2 and TensorFlow formats
支持Caffe,Caffe2和TensorFlow格式的模型
APIs for controlling loading, execution and scheduling on the runtimes
用于控制运行时的加载,执行和调度的API
Desktop tools for model conversion
用于模型转换的桌面工具
Performance benchmark for bottleneck identification
瓶颈识别的性能基准
Sample code and tutorials
示例代码和教程
HTML Documentation
HTML文档
To make the AI developer’s life easier, the Snapdragon NPE 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 Caffe/Caffe2 and TensorFlow 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 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.