How Much Do You Know About Embedded AI?

Question 1/7

What is a key challenge when deploying AI applications to embedded devices?

Excessive model accuracy

Lack of training data

Limited memory and computational resources

Too many cloud dependencies

Question 2/7

What is the key difference between embedded AI and edge AI?

Edge AI requires cloud connectivity; embedded AI does not

Embedded AI runs on resource-constrained hardware; edge AI may include more powerful local devices

Edge AI is only used in industrial applications

There is no difference; they are interchangeable terms

Question 3/7

What best describes the goal of tinyML?

To replace all cloud-based machine learning models with local models

To increase the size and complexity of machine learning models for better accuracy

To centralize data processing for improved data management

To enable machine learning on resource-constrained 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?

Manually rewrite the model logic in C/C++

Use a code generation tool (e.g., MATLAB Coder™) to automatically generate optimized C or C++ code for your microcontroller

Convert the model into a GPU-optimized run time

Compile the model using a Java^®-based deployment stack

Question 5/7

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™)

Manually rewrite the model in Java for GPU execution

Use HDL Coder™ to generate hardware logic for the GPU

Compile the model for FPGA deployment

Question 6/7

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

Export to JSON and write custom scripts to parse the model

Manually reenter the model weights as constants in a MATLAB script

This is only supported through Simulink^® simulation workflows

Question 7/7

Which technique can be used to reduce the size of AI models in preparation for embedded deployment?

Data augmentation

Gradient clipping

Ensemble learning

Quantization

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How Much Do You Know About Embedded AI?

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