AI software accelerator framework

Qualcomm Technologies, Inc. provides the following SDKs and tools to support hardware acceleration:

  • Qualcomm® Neural Processing SDK
  • AI Model Efficiency Toolkit (AIMET)

Qualcomm Neural Processing SDK

The Qualcomm Neural Processing SDK is designed to quickly allow developers to integrate AI/ML models to their Android apps. This is done by abstracting hardware complexities, providing the advantage of fast, portable AI application development.

Qualcomm Neural Processing SDK can be used for the following purposes:

  • Convert Caffe, Caffe2, TensorFlow, PyTorch and TFLite models to a Deep Learning Container (DLC) file
  • Quantize DLC files to 8bit/16bit fixed point for execution on the Qualcomm HTP
  • Integrate a network into Android apps via C++ or Java
  • Execute the network on the Qualcomm® Kryo™ CPU, the Qualcomm® Adreno™ GPU, or the HTP
  • Debug and analyze the performance of the ML model.

Below figure shows a typical workflow for AI development, model training is performed on any popular deep learning framework that is supported by the SDK. After training is complete the trained model is converted into a DLC file that can be loaded into the SDK runtime that runs on the target device.

Note: Qualcomm Neural Processing SDK for AI is also referred to as Snapdragon Neural Processing Engine (SNPE) in source code, tools, and documentation AIMET is a product of Qualcomm Innovation Center, Inc.

AI Model Efficiency Toolkit (AIMET)

AIMET is a library that provides advanced model quantization and compression techniques for trained neural network models.

It provides features that have been proven to improve run-time performance of deep learning neural network models with lower compute and memory requirements and minimal impact to task accuracy.

AIMET is designed to work with PyTorch and TensorFlow models.

Qualcomm Innovation Center also maintains an open-source repository called the AIMET Model Zoo at https://github.com/quic/aimet-model-zoo

AIMET Model Zoo is a collection of popular neural network models optimized for 8-bit inference. We also provide recipes for users to quantize floating point models using AIMET.

For more details on AIMET, please visit: https://github.com/quic/aimet For more details of AIMET model zoo, please visit: https://github.com/quic/aimet-model-zoo



Snapdragon and Qualcomm branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries. AIMET Model Zoo is a product of Qualcomm Innovation Center, Inc.