이 페이지는 기계 번역을 사용하여 번역되었습니다. 최신 내용을 영문으로 보려면 여기를 클릭하십시오.

임베디드 AI

딥러닝 네트워크와 Qualcomm® Hexagon® eNPU를 활용

Embedded Coder® Support Package for Qualcomm Hexagon Processors는 eAI 네트워크(컴파일된 eAI 모델)를 사용하여 eNPU의 응답을 예측하고 타깃에 배포하기 전에 eNPU의 정확성과 성능을 검증하기 위한 Simulink® 블록과 MATLAB® System object™를 제공합니다.

함수

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

블록

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

모델 설정

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

추천 예제

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.