Embedded AI is in consumer and industrial systems everywhere. How much do you know about embedded AI techniques and tools? Take this quiz—just 7 questions—to find out.
Question 1/7
What is a key challenge when deploying AI applications to embedded devices?
Question 4/7
You’re working with a resource-constrained microcontroller and need to deploy a trained AI model efficiently and in a short amount of time. What is a recommended approach?
Question 5/7
You want to accelerate a deep learning model for deployment on an NVIDIA® GPU. What is a practical strategy?
Question 6/7
How can you bring a trained PyTorch® or TensorFlow™ model into MATLAB® for embedded deployment?
Question 7/7
Which technique can be used to reduce the size of AI models in preparation for embedded deployment?
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- MATLAB and Simulink for Embedded AI - Overview
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Learn more about AI modeling, simulation, and deployment to embedded systems.
- MATLAB and Simulink for Embedded AI - Overview
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Explore pretrained AI models, a verification library, and resource-constrained deployment options.
- MATLAB and Simulink for Embedded AI - Overview
Answer Key
- What is a key challenge when deploying AI applications to embedded devices? Limited memory and computational resources
- What is the key difference between embedded AI and edge AI? Embedded AI runs on resource-constrained hardware; edge AI may include more powerful local devices
- What best describes the goal of tinyML? To enable machine learning on resource-constrained devices
- You’re working with a resource-constrained microcontroller and need to deploy a trained AI model efficiently and in a short amount of time. What is a recommended approach? Use a code generation tool (e.g., MATLAB Coder) to automatically generate optimized C or C++ code for your microcontroller
- You want to accelerate a deep learning model for deployment on an NVIDIA GPU. What is a practical strategy? Use a tool that generates CUDA code or deploys optimized models directly to the GPU (e.g., GPU Coder, TensorRT)
- How can you bring a trained PyTorch or TensorFlow model into MATLAB for embedded deployment? Use import tools provided by MATLAB to convert the models
- Which technique can be used to reduce the size of AI models in preparation for embedded deployment? Quantization
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