TinyML: Machine Learning on Microcontrollers Explained
2 Why Use TinyML?
1 Low Power Consumption: Can run for months or years on a battery.
2 Real-time Performance: Instant inference without sending data to the cloud.
3 Privacy: Data never leaves the device, improving security and privacy.
4 Offline Capabilities: Works without internet connectivity.
3 Where is TinyML Used?
1 Smartphones & Wearables: Gesture recognition, fall detection.
2 Home Automation: Wake-word detection (“Hey Alexa”), noise classification.
3 Agriculture: Monitoring crop conditions, soil sensors.
4 Industry/Manufacturing: Predictive maintenance, anomaly detection.
5 Healthcare: Portable diagnostic tools.
4 Typical TinyML Hardware
Microcontrollers like:
1 Arduino Nano 33 BLE Sense
2 Raspberry Pi Pico
3 ESP32
4 STM32 series
These devices usually have:
1 CPU: 32-bit ARM Cortex-M
2 RAM: ~64KB–256KB
3 Flash: ~256KB–1MB
5 Common TinyML Frameworks
1 TensorFlow Lite for Microcontrollers (TFLM)
A stripped-down version of TensorFlow designed to run on microcontrollers.
2 Edge Impulse
Cloud platform for building and deploying embedded ML.
3 CMSIS-NN
Neural network kernels optimized for Arm Cortex-M CPUs.
4 uTensor / microTVM
Other lightweight inference engines.
6 Typical Workflow
1 Collect Data (sensor data like audio, motion, etc.)
2 Train Model (on a laptop/cloud using TensorFlow or similar)
3 Quantize Model (reduce model size using int8/float16)
4 Deploy to Microcontroller (using TFLM or similar)
5 Run Inference On-Device (real-time predictions)
7 Challenges of TinyML
1 Limited compute and memory resources
2 Efficient model quantization without losing too much accuracy
3 Power management for battery life
4 Difficulties in debugging and profiling
Future of TinyML
TinyML is revolutionizing embedded systems by making them smarter and more autonomous. As hardware becomes more capable and ML models more efficient, expect to see intelligent features embedded in more devices—right at the edge.
