frameworks.qmd

# AI Frameworks

## Introduction

Explanation: Discuss what ML frameworks are and why they are important. Also, elaborate on the aspects involved in understanding how an ML framework is developed and deployed.

- Definition of ML Frameworks
- What is an embedded ML framework?
- Why are embedded ML frameworks important?
- Challenges of embedded ML
- Benefits of using embedded ML frameworks, trade-offs, and differences.

## Typical ML Frameworks

Explanation: Discuss the most common types of ML frameworks available and provide a high-level overview, so that we can set into motion what makes embedded ML frameworks unique.

- TensorFlow, PyTorch, Keras, ONNX Runtime, Scikit-learn
- Key Features and Advantages
- API and Programming Paradigms

## Constraints for Embedded AI

Explanation: Describe the constraints of embedded systems, referring to the previous chapters, and remind readers about the challenges and why we need to consider creating lean and efficient solutions.

### Hardware

- Memory Usage
- Processing Power
- Energy Efficiency
- Storage Limitations
- Hardware Diversity

### Software

- Library Dependency
- Lack of OS

## Embedded AI Frameworks

Explanation: Now, discuss specifically about the unique embedded AI frameworks that are available and why they are special, etc.

- TensorFlow Lite
- ONNX Runtime
- MicroPython
- CMSIS-NN
- Edge Impulse
- Others (briefly mention some less common but significant frameworks)

## Framework Comparison

Explanation: Provide a high-level comparison of the different frameworks based on class slides, etc.

- Table of differences and similarities

## Toolchain Integration

Explanation: Help people understand that it's more than just the framework, and that elements need to fit into the ecosystem of various aspects that exist in an embedded system.

- Compatibility with Embedded Development Environments
- Integration with Firmware and Hardware

## Trends in ML Frameworks

Explanation: Discuss where these ML frameworks are heading in the future. Perhaps consider discussing ML for ML frameworks?

- Framework Developments on the Horizon
- Anticipated Innovations in the Field

## Conclusion

- Summary of Key Takeaways
- Recommendations for Further Learning