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September 18, 2026
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September 18, 2026
The Tech Trends
AI
AI Ethics
Automation
Deep Learning
Generative AI
Machine Learning
Robotics
Culture
Creator Economy
Digital Nomads
Internet Culture
Remote Work
Tech Careers
Tech Events
Future Trends
5G/6G Networks
BioTech
Metaverse
Quantum Computing
Space Tech
Sustainable Tech
Innovation
AgriTech
EdTech
FinTech
Green Tech
HealthTech
Smart Cities
Gadgets
AR/VR Devices
Drones
Health Tech
Smart Home
Smartphones
Wearables
Software
App Development
Cloud Computing
Cybersecurity
Open Source
Productivity Tools
SaaS
Startups
Disruptive Ideas
Founder Stories
Funding News
Startup Trends
Tech Launches
Unicorn Watch
Web3
Blockchain
Cryptocurrency
DAOs
Decentralization
NFTs
Smart Cities
×
AI
The Tech Trends
AI
AI
Machine Learning
TinyML Guide: Running Machine Learning on Microcontrollers for IoT
by
Oliver Grant
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Ayman Haddad
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Table of Contents
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Key Takeaways
What is TinyML?
The Scope of TinyML
Why Run ML on Microcontrollers?
1. Bandwidth and Data Tsunami
2. Latency and Real-Time constraints
3. Energy Efficiency
4. Privacy and Security
5. Reliability
How TinyML Works: The Architecture
Phase 1: Data Collection and Engineering
Phase 2: Model Training
Phase 3: Model Compression
Phase 4: Compilation and Deployment
The Hardware Landscape: Where TinyML Lives
1. ARM Cortex-M Series
2. Espressif Systems (ESP32)
3. Specialized AI Microcontrollers
4. RISC-V
Core Techniques: Pruning and Quantization Explained
Quantization: The Art of Approximation
Pruning: Cutting the Dead Wood
Software Frameworks and Tools
TensorFlow Lite for Microcontrollers (TFLM)
Edge Impulse
MicroTVM (Apache TVM)
Real-World Use Cases
1. Predictive Maintenance (Industrial IoT)
2. Audio Analytics and Keyword Spotting
3. Smart Agriculture / Wildlife Conservation
4. Health and Wearables
Tutorial: Conceptual Workflow for Building a TinyML Project
Step 1: Requirements and Hardware
Step 2: Data Collection
Step 3: Signal Processing
Step 4: Training the Model
Step 5: Optimization
Step 6: Deployment (Inference)
Challenges and Limitations
1. Memory Constraints
2. Lack of Debugging Tools
3. Hardware Fragmentation
4. Over-the-Air (OTA) Updates
The Future of TinyML
Who is this for? (And who it isn’t)
Conclusion
Next Steps
FAQs
References
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Table of Contents