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고속 처리율 컴퓨팅 애플리케이션
고속 처리율 컴퓨팅 애플리케이션을 위한 코드 생성
Embedded Coder®를 사용하여 딥러닝 네트워크, 이미지 처리 및 컴퓨터 비전 애플리케이션, 신호 처리 시스템을 위한 코드를 생성합니다. OpenMP(Open Multiprocessing) 및 SIMD 코드를 생성하여 이러한 고속 처리율 컴퓨팅 애플리케이션의 실행 속도를 개선합니다.
도움말 항목
고속 처리율 컴퓨팅 최적화 기술
- Generate SIMD Code from Simulink Blocks for Intel Platforms
Improve the execution speed of the generated code using Intel® SSE and Intel AVX technology. - Generate SIMD Code from MATLAB Functions for Intel Platforms
Improve the execution speed of the generated code using Intel SSE and Intel AVX technology. - Generate Parallel for-Loops Using the Open Multiprocessing (OpenMP) Application Interface
Implement parallel for-loops in the generated code for For Each Subsystems, MATLAB Function and MATLAB System blocks.
딥러닝
- Workflow for Deep Learning C/C++ Code Generation for Simulink Models
Overview of C/C++ code generation workflow for deep learning neural networks. - Generate Code for Deep Learning Networks Using MATLAB Function Block
Generate code for a model containing a MATLAB Function block that uses the GoogLeNet trained deep learning network. - Generate Code for Blocks from Deep Neural Networks Library
Generate code for a model containing the GoogLeNet trained deep learning network. - Code Generation for Deep Learning Simulink Model That Performs Lane and Vehicle Detection
This example shows how to generate C++ code from a Simulink® model that performs lane and vehicle detection using convolutional neural networks (CNN). - Generate Generic C/C++ for Sequence-to-Sequence Deep Learning Simulink Models
Generate C/C++ code for a sequence-to-sequence deep learning Simulink model. - Generate Generic C Code Using the Stateful Predict Block in Simulink
This example shows how to generate generic C code using the Stateful Predict block and the SIL workflow. (R2024a 이후) - Call Generated Code Using C Caller Blocks
Call generated C code from the Simulink model by using a C Caller block. (R2025a 이후) - Simulink Simulation of Deep Learning Models Using MATLAB Function Block
Simulate model that predicts responses for a LSTM network using a MATLAB Function block. (R2025a 이후) - Update the Network Learnables for a Battery State of Charge Estimation Model
Update the learnables of a deep learning network while the Simulink model is simulating. (R2025a 이후)
신호 처리
- 보간된 FIR 필터에 대한 코드 생성 (DSP System Toolbox)
종속적으로 연결된 멀티레이트 다단 필터를 사용하여 높은 차수의 FIR 필터를 설계하고 구현합니다. 이 필터에서 코드를 생성하고, 코드 파일을 패키징합니다. - Generate and Deploy SIMD Optimized Code for Interpolated FIR Filter on Intel Desktops (DSP System Toolbox)
Generate and deploy optimized code for an interpolated finite impulse response (IFIR) filter within an Intel desktop environment using Simulink. - Use Target Hardware Instruction Set Extensions to Generate SIMD Code from Simulink Blocks for ARM Cortex-A Processors (DSP System Toolbox)
Generate high performance SIMD Code from Simulink® Blocks in DSP System Toolbox™ by using the Embedded Coder Support Package for ARM® Cortex®-A Processors. - Use Intel AVX2 Code Replacement Library to Generate SIMD Code from Simulink Blocks (DSP System Toolbox)
Generate high performance SIMD code from Simulink blocks in DSP System Toolbox using Intel AVX2 code replacement library. - Use Intel AVX2 Code Replacement Library to Generate SIMD Code from MATLAB Algorithms (DSP System Toolbox)
Generate high performance SIMD Code from MATLAB® algorithms in DSP System Toolbox using Intel AVX2 code replacement library.
컴퓨터 비전 및 이미지 처리
- Accelerate Pedestrian Detection with SIMD Code
Generate SIMD code to improve the quality and speed of a pedestrian detection and tracking system. - Accelerate Vehicle Detection with SIMD
Generate SIMD code to improve the quality and speed of a vehicle detection and tracking system. - Automatically Schedule for-Loops for Neighborhood Processing Subsystems
Automatically schedule for-loop nests in the generated code for Neighborhood Processing blocks.
관련 정보
- Simulink를 사용한 딥러닝 (Deep Learning Toolbox)
- MATLAB Coder를 사용한 딥러닝
- GPU Coder를 사용한 딥러닝 (GPU Coder)
