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組み込み AI

深層学習ネットワークと Qualcomm® Hexagon® eNPU で作業する

Embedded Coder® Support Package for Qualcomm Hexagon Processors には、eAI ネットワーク (コンパイルされた eAI モデル) を使用して eNPU の応答を予測し、ターゲットに展開する前に eNPU の精度とパフォーマンスを検証するための Simulink® ブロックと MATLAB® System object™ が用意されています。

関数

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

ブロック

eNPU PredictPredict responses of eNPU using compiled eAI model (R2025a 以降)

モデル設定

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

注目の例

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.