DOWNLOAD A FREE TRIALDeploy machine learning and deep learning applications to embedded systems
With MATLAB and Simulink, you can design, simulate, test, verify, and deploy AI algorithms that enhance the performance and functionality of complex embedded systems.
Why MATLAB and Simulink?
Deploy to CPUs and Microcontrollers
Generate portable, optimized C/C++ code from trained machine learning and deep learning models with MATLAB Coder and Simulink Coder. Some examples are:
- Developing and Embedding AI-Based SOC Estimation for BMS Using MATLAB
- Develop and Deploy a Neural Network for MCUs
- Generate Generic C/C++ Code for Deep Learning Networks
- Code Generation for LSTM Network on Raspberry Pi
Deploy to GPUs
Generate optimized CUDA® code for trained deep learning networks with GPU Coder for deployment to desktops, servers, and embedded GPUs. Some use cases are:
- Code Generation for Detecting Defects on Printed Circuit Boards Using YOLOX Network
- Deep Learning Prediction by Using NVIDIA TensorRT
- Code Generation for a Deep Learning Simulink Model to Classify ECG Signals
Deploy to FPGAs and SoCs
Prototype and implement deep learning networks on FPGAs and SoCs with Deep Learning HDL Toolbox. Generate custom deep learning processor IP cores and bitstreams with HDL Coder.
- Use MATLAB to Prototype Deep Learning on a Xilinx FPGA
- Use MATLAB to Prototype Deep Learning on an Altera FPGA
- Deploy Neural Network Regression Model to FPGA/ASIC Platform
Deploy to NPUs
Generate optimized code for NPUs like Qualcomm Hexagon and Infineon PPU in AURIX TC4x.
- Accelerate Edge AI with the Hexagon Hardware Support Package
- Qualcomm Hexagon Hardware Support from Embedded Coder
- Infineon AURIX Hardware Support from Simulink
- Accelerate AI Based Software Development on Infineon AURIX TC4x Microcontroller
AI Model Compression
Compress deep neural networks with quantization, projection, or pruning to reduce memory footprint and increase inference performance.
- Reduce Memory Footprint of Deep Neural Networks
- A Productive Journey to Deploy Tiny Neural Networks on Microcontrollers
- Compress Image Classification Network for Deployment to Resource-Constrained Embedded Devices
AI Verification
AI verification applies rigorous methods like the W-shaped process to ensure intended behaviors and prevent unintended ones.
- Machine Learning Models for Safety-Critical Systems
- Verify an Airborne Deep Learning System
- AI Verification Library
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