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딥러닝 코드 생성 기초
MATLAB® Coder™를 Deep Learning Toolbox™와 함께 사용하여 훈련된 CNN에서 C++ 코드를 생성할 수 있습니다. 그런 다음 생성 코드를 Intel® 프로세서 또는 ARM® 프로세서를 사용하는 임베디드 플랫폼에 배포할 수 있습니다. 타사 라이브러리에 의존하지 않는 훈련된 CNN에서 일반 C 또는 C++ 코드를 생성할 수도 있습니다.
함수
codegen | MATLAB 코드에서 C/C++ 코드 생성 |
coder.loadDeepLearningNetwork | Load deep learning network model |
coder.DeepLearningConfig | Create deep learning code generation configuration objects |
coder.DeepLearningCodeConfig | Parameters to configure deep learning code generation that does not depend on third-party libraries (R2021a 이후) |
coder.ai.enableParameterUpdate | Enables run-time update of network parameters (R2025a 이후) |
coder.ARMNEONConfig | Parameters to configure deep learning code generation with the ARM Compute Library |
coder.CMSISNNConfig | Parameters to configure deep learning code generation with the CMSIS-NN library for Cortex-M targets (R2022a 이후) |
coder.MklDNNConfig | Parameters to configure deep learning code generation with the Intel Math Kernel Library for Deep Neural Networks |
analyzeNetworkForCodegen | Analyze deep learning network for code generation (R2022b 이후) |
coder.regenerateDeepLearningParameters | Regenerate files containing network learnables and states parameters (R2021b 이후) |
코드 구성 파라미터
| 딥러닝 타깃 라이브러리 | Target library for deep learning code generation |
| 학습 가능 압축 | Compression type |
| ARM Compute Library 버전 | Version of ARM Compute Library |
| ARM Compute 아키텍처 | ARM architecture supported in the target hardware |
| 데이터형(ARM Compute) | Inference computation precision |
| 보정 결과 파일 경로(ARM Compute) | Location of calibration MAT file |
| 데이터형(CMSIS-NN) | Inference computation precision |
| 보정 결과 파일 경로(CMSIS-NN) | Location of calibration MAT file |
도움말 항목
- Prerequisites for Deep Learning with MATLAB Coder
Install products and configure environment for code generation for deep learning networks.
- MATLAB Coder를 사용한 딥러닝 코드 생성 워크플로
사전 훈련된 신경망에서 예측을 위한 코드를 생성합니다.
- Load Pretrained Networks for Code Generation
Create a
dlnetworkobject, or an object detector for code generation. - Analyze Network for Code Generation
Check code generation compatibility of a deep learning network.
- Networks and Layers Supported for Code Generation
Choose a convolutional neural network that is supported for your target processor.
- Code Generation for dlarray
Use deep learning arrays in MATLAB code intended for code generation.
- dlarray Limitations for Code Generation
Adhere to code generation limitations for deep learning arrays.
- Generate Generic C/C++ Code for Deep Learning Networks
Generate C/C++ code for prediction from a deep learning network that does not depend on third-party libraries.
- Update Network Parameters at Run Time
Update deep learning network parameters at run-time without regenerating code.
- Compress Networks Learnables in bfloat16 Format
Perform learnables compression and generate C/C++ code in Brain Floating Point format, bfloat16.
- Optimize C/C++ Code Performance for Deep Learning Applications without Deep Learning Libraries
Code generation configuration settings that optimize the performance of the generated C/C++ code for a deep learning network.
- Code Generation for Deep Learning Networks with MKL-DNN
Generate C++ code for prediction from a deep learning network, targeting an Intel CPU.
- Code Generation for Deep Learning Networks with ARM Compute Library
Generate C++ code for prediction from a deep learning network, targeting an ARM processor.
- Cross-Compile Deep Learning Code That Uses ARM Compute Library
Generate library or executable code on host computer for deployment on ARM hardware target.
- Generate int8 Code for Deep Learning Networks
Quantize and generate code for a pretrained convolutional neural network.
- Update Network Parameters After Code Generation
Perform post code generation updates of deep learning network parameters.
관련 정보
- Deep Learning Toolbox 시작하기 (Deep Learning Toolbox)
- GPU Coder를 사용한 딥러닝 (GPU Coder)
추천 예제
Generate Digit Images Using Variational Autoencoder on Intel CPUs
Generate code for a trained VAE dlnetwork to generate hand-drawn digits.
Cross Compile Deep Learning Code for ARM Neon Targets
Generate library or executable code on host computer for deployment on ARM hardware target.
Post-Code-Generation Update of Deep Learning Network Parameters
Incrementally update the network learnables of a deep learning network application running on edge devices such as Raspberry Pi®. This example uses a cart-pole reinforcement learning application to illustrate:
