Applicazioni di calcolo ad alta produttività
Generare codice per applicazioni di calcolo ad alta produttività
Utilizzare Embedded Coder® per generare codice per reti di Deep Learning, applicazioni di elaborazione di immagini e Computer Vision e sistemi di elaborazione dei segnali. Migliorare la velocità di esecuzione di queste applicazioni di calcolo ad alta produttività generando codice OpenMP (Open Multiprocessing) e SIMD.
Argomenti
Tecniche di ottimizzazione del calcolo ad alta produttività
- 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.
Deep Learning
- 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. (Da R2024a) - Call Generated Code Using C Caller Blocks
Call generated C code from the Simulink model by using a C Caller block. (Da R2025a) - Simulink Simulation of Deep Learning Models Using MATLAB Function Block
Simulate model that predicts responses for a LSTM network using a MATLAB Function block. (Da R2025a) - 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. (Da R2025a)
Elaborazione del segnale
- Code Generation for Interpolated FIR Filter (DSP System Toolbox)
Use a cascade of multirate multistage filters to design and implement a high order FIR filter. Generate code from this filter and package the code files. - 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.
Computer Vision ed elaborazione di immagini
- 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.
Informazioni complementari
- Deep Learning con Simulink (Deep Learning Toolbox)
- Deep Learning con MATLAB Coder
- Deep Learning with GPU Coder (GPU Coder)
