ai-ml-developer-workflow

# Overview Join us and watch this training session focused on the Qualcomm Linux AI Developer Workflow. It is tailored for developers and professionals in the IoT sector who are keen to enhance their understanding and skills in deploying on-device AI solutions on a Qualcomm platform. The AI/ML developer workflow on Qualcomm Linux has two major steps: | Step 1

Compile and optimize a model |

Compile and optimize the model from the third-party AI
framework to efficiently run on Qualcomm hardware. For
example, a Tensorflow model can be exported to a LiteRT
model.

Optionally, quantize, fine-tune performance, and accuracy
using hardware-specific customizations.

| | --- | --- | | Step 2

Build an application to use the optimized model to run on
device inference |

Integrate the AI model into the use case pipeline.

Cross-compile the application to generate an executable
binary using dependent libraries.

| Important - Ensure that the host computer uses Ubuntu 22.04. - The commands in this document are compatible with Qualcomm Linux 1.4. Verify your Qualcomm Linux release version by running the commands described in the [Dev Kit Quick Start guide](https://docs.qualcomm.com/bundle/publicresource/topics/80-70018-253/getting_started.html) If your release version isn’t 1.4, [update your software](https://docs.qualcomm.com/bundle/publicresource/topics/80-70018-253/set_up_the_device.html#panel-0-VWJ1bnR1tab$update-software). - Sample applications and AI procedures in this document are compatible with the [supported versions](https://docs.qualcomm.com/bundle/publicresource/topics/80-70018-51/introduction.html#supported-component-versions). Ensure you download the matching SDKs to your host computer before starting AI/ML development. ## AI architecture Developers can bring models from ONNX, PyTorch, TensorFlow or TFLite and run them efficiently on Qualcomm AI Hardware–HTP (NPU), GPU, CPU using Qualcomm AI SDKs. ## AI hardware - **Qualcomm Kryo™ CPU**: Best-in-class CPU with high performance and remarkable power efficiency. - **Qualcomm Adreno GPU**: Suitable to run AI workloads with balanced power and performance. AI workloads are accelerated with OpenCL kernels. The GPU can also be used to accelerate model pre/postprocessing. - **Qualcomm Hexagon Tensor Processor (HTP)**: Also known as NPU/DSP/HMX, suitable to run AI workloads with low-power and high-performance. For optimized performance, pretrained models need be quantized to one of the supported precisions. ## AI software AI stack contains SDKs to harness the power of AI hardware accelerators. Developers can use one of the SDKs of their choice to deploy AI workloads. Pretrained models (except for LiteRT models) need to be converted to an executable format with the selected SDK before running them. LiteRT models can be run directly using TFLite Delegate. TFLite LiteRT models can be natively run on Qualcomm hardware with acceleration using the following Delegates. | Delegate | Acceleration | | --- | --- | | AI Engine Direct Delegate (QNN Delegate) | CPU, GPU and HTP | | XNNPACK Delegate | CPU | | GPU Delegate | GPU | Qualcomm Neural Processing Engine SDK (SNPE) Qualcomm Neural Processing Engine (SNPE) is a software accelerated runtime for execution of deep neural networks. SNPE offers tools to convert, quantize neural networks and accelerate them on hardware accelerators including CPU, GPU, and HTP. Qualcomm AI Engine Direct (QNN) Qualcomm AI Engine Direct is a software architecture for AI/ML use cases on Qualcomm chipsets and AI acceleration cores. The architecture is designed to provide a unified API and modular and extensible per-accelerator libraries, which form a reusable basis for full stack AI solutions. It provides support for runtimes such as Qualcomm Neural Processing SDK, LiteRT AI Engine Direct Delegate. AI Model Efficiency Toolkit (AIMET) Open-source library to optimize (compress and quantize) trained neural network models. This is a complex SDK designed to generate optimized quantized models and is intended only for advanced developers. Last Published: Oct 13, 2025 [Previous Topic AI developer workflow documentation](https://docs.qualcomm.com/bundle/publicresource/80-70018-15B/topics/introduction.md) [Next Topic Compile and optimize an AI model](https://docs.qualcomm.com/bundle/publicresource/80-70018-15B/topics/compile-and-optimize-model.md)