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72 lines
2.2 KiB
Plaintext
72 lines
2.2 KiB
Plaintext
# ML Frameworks
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## Introduction
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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.
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- Definition of ML Frameworks
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- What is an embedded ML framework?
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- Why are embedded ML frameworks important?
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- Challenges of embedded ML
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- Benefits of using embedded ML frameworks, trade-offs, and differences.
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## Typical ML Frameworks
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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.
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- TensorFlow, PyTorch, Keras, ONNX Runtime, Scikit-learn
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- Key Features and Advantages
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- API and Programming Paradigms
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## Constraints for Embedded AI
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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.
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### Hardware
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- Memory Usage
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- Processing Power
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- Energy Efficiency
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- Storage Limitations
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- Hardware Diversity
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### Software
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- Library Dependency
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- Lack of OS
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## Embedded AI Frameworks
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Explanation: Now, discuss specifically about the unique embedded AI frameworks that are available and why they are special, etc.
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- TensorFlow Lite
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- ONNX Runtime
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- MicroPython
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- CMSIS-NN
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- Edge Impulse
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- Others (briefly mention some less common but significant frameworks)
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## Framework Comparison
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Explanation: Provide a high-level comparison of the different frameworks based on class slides, etc.
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- Table of differences and similarities
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## Toolchain Integration
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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.
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- Compatibility with Embedded Development Environments
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- Integration with Firmware and Hardware
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## Trends in ML Frameworks
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Explanation: Discuss where these ML frameworks are heading in the future. Perhaps consider discussing ML for ML frameworks?
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- Framework Developments on the Horizon
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- Anticipated Innovations in the Field
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## Conclusion
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- Summary of Key Takeaways
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- Recommendations for Further Learning |