嵌入式 AI

使用深度学习网络和 Qualcomm® Hexagon® eNPU

Embedded Coder® Support Package for Qualcomm Hexagon Processors 提供 Simulink® 模块和 MATLAB® System object™，以使用 eAI 网络（编译的 eAI 模型）预测 eNPU 的响应，并在部署到目标之前验证 eNPU 的准确性和性能。

函数

hexagon.ENPUInterface to predict responses of eNPU using a compiled eAI model (自 R2025a 起)
hexagon.ENPU.getEAIPropertiesRead values for properties defined in eAI network file (自 R2025a 起)

模块

eNPU PredictPredict responses of eNPU using compiled eAI model (自 R2025a 起)

模型设置

Model Settings to Deploy Code for eNPU Module of Qualcomm Hexagon Processor

精选示例

Deploy Smart Speaker Model on Qualcomm Hexagon eNPU using LPAI SDK Add-on

Deploy Smart Speaker Model on Qualcomm Hexagon eNPU using LPAI SDK Add-on

Deploy a Simulink® model designed as smart speaker system on Qualcomm® Hexagon® eNPU using Embedded Coder® Support Package for Qualcomm Hexagon Processors. This example is based on the Simulink model available in Apply Speech Command Recognition Network in Smart Speaker Simulink Model (Audio Toolbox) example. In the updated model this example, the eNPU Predict block replaces the DL predict block from the original example as this block uses a precompiled eAI model for deploying the DL network to Hexagon eNPU.

Convert MATLAB Deep Learning Networks to eAI Model Using Qualcomm LPAI SDK

Convert MATLAB Deep Learning Networks to eAI Model Using Qualcomm LPAI SDK

Convert Deep Learning networks developed using MATLAB® built-in layers into eAI models for deployment to eNPU using Qualcomm® LPAI SDK. Deploying to an eNPU (LPAI) requires the Deep Learning network to be in the .eai file format (eAI model), generated using eai_builder tool from the LPAI SDK. This SDK provides a workflow for converting a ONNX/TFLite models to this format. The workflow explained in this example helps you to streamline the process for conversion of MATLAB-based Deep Learning networks to the .eai format.