ABSTRACT: Artificial intelligence is no longer confined to the cloud. Thanks to Edge AI, we can now run AI models directly on embedded devices with limited power and resources. This session will explore the full pipeline of developing a Tiny Machine Learning (TinyML) model, from data collection to deployment, addressing key challenges such as dataset preparation, model training, quantization, and optimization for embedded systems. We’ll explore real-world use cases where AI-powered embedded systems enable smart decision-making in applications like predictive maintenance, anomaly detection, and voice recognition. The talk will include a live hands-on demonstration on how to train and deploy a model using popular tools like Google Colab and TensorFlow, and then run real-time inference on an Arduino board. BIO: Leonardo Cavagnis is an experienced embedded software engineer, interested in IoT and AI applications. At Arduino, he works as a firmware engineer, developing libraries and core functionalities for boards while also focusing on communication and engaging with the community.

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2. Trento 󰏢, April 9th, 2025
Edge AI 🪄
Bringing Intelligence to
Embedded Devices
Leonardo Cavagnis
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Whoami
Leonardo Cavagnis
Sr. Firmware Engineer @ Arduino
Community Builder @ ItalianEmbedded
🧡 Passionate about sharing knowledge

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Agenda
➔ Edge AI Basics ✨
➔ Building an AI/ML Model 🧠
➔ Hands-on Demo 󰞵

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Introduction to Edge AI
✨

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What is Edge AI ?
AI that runs directly on the device
Data processing
near the source,
directly on devices
Algorithms to simulate
the human cognitive
process
Traditional Cloud AI

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Why Edge AI Matters?
Low Latency Energy Saving
Processing locally
reduces delay
compared to Cloud AI
Offline
Since computations
happen locally, the
device can function
without an internet
connection
Privacy
Minimizing the need to
send sensitive data to
the Cloud
Reducing data
transmission and cloud
dependency lowers
power consumption

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Edge AI in Action
Healthcare
Wearable devices,
Remote patient
monitoring
Smart Home
Voice assistants,
Smart appliances
Industrial
Predictive maintenance,
Anomaly detection
Automotive
ADAS,
Autonomous vehicle

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Building an AI Model 󰠻

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A Simplified View
Deployment
Model
Testing
Data
Collection
Problem
Definition
Model
Training
“Identify if a 😽 is
present in an image”
󰢛
The AI learns to
recognize cats in
images
✅❌
Evaluate model
performance
using new cat
images

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What is Machine Learning (ML)?
Artificial Intelligence
Machine
Learning
Creating machines simulating
human intelligence
Algorithms enabling computers to
learn from data
Supervised
Learning
Unsupervised
Learning
Classifying cat vs no-cat images
using labeled data
Clustering animal images based
on visual features without labels

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Neural Networks & Deep Learning
Computational models with
interconnected artificial neurons,
inspired by the brain 🧠
Subset of ML where
neural networks learn from large dataset
Machine Learning
Neural
Networks
Deep
Learning

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Neural Network: The basics
An Artificial Neural Network (ANN) consists of layers of neurons that process information
- Each neuron is a unit that performs calculations
Neuron
Input
tensor
x
Weights
W ∑
Bias
b
Activation
function
f(z)
Output
tensor
y
x[] y[]
z = W⋅x + b
y = f(z)

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Introduction to Tiny ML
Embedded
System
Machine
Learning
What is an Embedded System?
Devices with limited resources
designed to perform specific tasks
Tiny ML
Application of Machine Learning on
embedded devices (e.g., microcontrollers)
Uses techniques to
reduce model size:
🔢 Quantization
✂ Pruning
🧪 Distillation
…

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Edge AI, Edge ML & Tiny ML
Edge AI
Edge ML
Tiny ML
AI processing on edge devices
Machine Learning on edge devices
Machine Learning on low-power embedded devices

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Hands-on Demo 󰞵

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ML Magic Wand 🪄
A gesture recognition system based on a Machine Learning Model using Movement sensor data.

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ML Magic Wand 🪄
Sensor data are processed by a model to recognize 2 gestures, using Arduino and TensorFlow.
🧠Arduino
Nano Matter
Board
Movement
Sensor
Board
💡Color bar
Board
Ring
gesture
Wing
gesture
3D printed
carrier

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What is
Arduino?
󰢃

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What is Arduino? 👍
Arduino is a global leader
in open-source hardware and software
It provides a complete platform for making
interactive projects
Designed to be accessible to all
Large and active community (> 30M developers)

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Arduino Mission
“Enable anyone to innovate by making complex technologies simple to use”
Student Maker Business

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The setup: TensorFlow + Colab + Python
An open-source ML
framework by Google,
enabling the development
and deployment of AI models
from cloud to edge.
A cloud-based Jupyter
Notebook to write and
execute Python code easily
and quickly.
+ =
Easy-to-use,
HW-Accelerated
environment to
build AI model

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Recall: The Pipeline
Deployment
Model
Testing
Data
Collection
Problem
Definition
Model
Training

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Classification problem
Deployment
Model
Testing
Data
Collection
Problem
Definition
Model
Training
The problem involves gesture recognition using
accelerometer data from an IMU sensor.
The goal is to classify 2+1 gestures:
1. Wing (W)
2. Ring (O)
3. No Gesture (idle or unrecognized movements)
👀 This is a Supervised Learning classification problem.
TinyML Model
Probabilities
[Wing, Ring, No Gesture]
Accelerometer data
[P1,P2,P3]
[ax, ay, az]

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Define model input
Deployment
Model
Testing
Data
Collection
Model
Training
Problem
Definition
TinyML Model
IMU sensor readings frequency = 100 Hz
Sample duration = 2 secs approx.
2s * 100 Hz = 200
200 tuples of accelerometer [aX,aY,aZ] sensor data
for each sample
→ a total of 600 (200 * 3) data points/sample
Accelerometer data
recorded for 2 secs
200 * [aX, aY, aZ]

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Data preprocessing
Deployment
Model
Testing
Data
Collection
Model
Training
Problem
Definition
Collected raw data is processed to make it suitable for model training:
- Normalization
- Noise filtering
- Interpolation
- Feature Extraction
- …
normalized_value = (raw_value_acc + 2000) / 4000
Normalization
Scaling data to specific range, typically [0,1] → preventing large values and improve stability
Accelerometer data is expressed in milliG (mG) in in a range [-2000, +2000]

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Let’s collect the data!
Deployment
Model
Testing
Data
Collection
Model
Training
Problem
Definition
Perform multiple Wing (W) and Ring (O) gestures, including unrecognized movements.
1⃣ Wait for a movement
→ The system monitors sensor data
until a threshold is exceeded.
2⃣ Start recording
→ Data is collected for 2 seconds.
3⃣ Output the samples
→ The recorded data is printed to the
Serial monitor in CSV format.

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Dataset splitting
Deployment
Model
Testing
Data
Collection
Model
Training
Problem
Definition
Dataset is divided into 3 subsets:
1. Training [60%]
Train the machine learning model.
2. Validation [20%]
Monitor its performance during training.
3. Test [20%]
Evaluate the model's performance after training.
Validation Test
Training

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Model Architecture
Deployment
Model
Testing
Data
Collection
Model
Training
Problem
Definition
Training: model learns patterns and relationships in the data to make predictions.
The Multi-Layer Perceptron (MLP) is the simplest ANN architecture for classification tasks.
Features
✅ Fully Connected Layers
✅ At Least One Hidden Layer
✅ Non-Linear Activation Functions

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Model Architecture with TensorFlow
Deployment
Model
Testing
Model
Training
Problem
Definition
A Multi-Layer ANN can be defined using:
import tensorflow as tf
model = tf.keras.Sequential()
Data
Collection
For our problem, we can design a network with:
● 256 neurons in the hidden layer
model.add(tf.keras.layers.Dense(256, activation='relu'))
● 3 neurons in the output layer, each representing one of the 3 gestures to classify
model.add(tf.keras.layers.Dense(3, activation='softmax'))
…
Wing
Ring
None
600 = 200 * [aX,aY,aZ]
256
3

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Model Architecture with TensorFlow
Deployment
Model
Testing
Model
Training
Problem
Definition
A typical Multi-Layer Perceptron (MLP) architecture consists of two hidden layers.
import tensorflow as tf
model = tf.keras.Sequential()
Data
Collection
For our problem, we can design a network with:
● 256 neurons in the first hidden layer
model.add(tf.keras.layers.Dense(256, activation='relu'))
● 128 neurons in the second hidden layer
model.add(tf.keras.layers.Dense(128, activation='relu'))
● 3 neurons in the output layer, each representing one of the 3 gestures to classify
model.add(tf.keras.layers.Dense(3, activation='softmax'))
…
…
Wing
Ring
None
600 = 200 * [aX,aY,aZ]

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Model Training with TensorFlow
Deployment
Model
Testing
Model
Training
Problem
Definition
Set training parameters:
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
…Let’s start the training!
model.fit(inputs_train, outputs_train,
epochs=100, batch_size=8,
validation_data=(inputs_validate, outputs_validate))
Validation Test
Training
Data
Collection

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Monitoring Training Results
Deployment
Model
Testing
Model
Training
Problem
Definition
Monitor the loss and accuracy curves for both the training and validation data:
Epoch 3/100
32/32 [==============================] -
0s 8ms/step - loss: 1.0856 - accuracy: 0.4233 - val_loss: 1.0800 - val_accuracy:
0.4200
If the loss decreases while
the accuracy increases
→ the model is learning well ✅
Data
Collection

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Evaluate Model Performance
Deployment
Model
Testing
Model
Training
Problem
Definition
We need to check how well the model generalizes to unseen data.
This is done by evaluating its predictions on the test dataset.
predictions = model. predict(inputs_test)
One useful method is to analyze the
Confusion Matrix
Validation Test
Training
Data
Collection

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From “Big” to Tiny
Deployment
Model
Testing
Model
Training
Problem
Definition
Data
Collection
To run the model on a microcontroller, we need to convert it to a TinyML model
using TensorFlow Lite.
converter = tf.lite. TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize. DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet. TFLITE_BUILTINS_INT8]
tflite_model = converter. convert()
Integer Quantization
Converting float to nearest
8-bit fixed-point numbers
⚡Optimizations:
- LATENCY
- SIZE
- DEFAULT (trade-off)

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FlatBuffer: Implementing ANN on MCU
Deployment
Model
Testing
Model
Training
Problem
Definition
Data
Collection
FlatBuffer is commonly used to store neural network models in a
compact, fast-access structure.
010
101
FlatBuffer
Efficient data serialization format
1. Header
Model metadata
2. Operators
A list of operations
3. Tensors
Data structure for inputs/outputs
4. Parameters
Numerical values used in the inference

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Let’s code!
Deployment
Model
Testing
Model
Training
Problem
Definition
Data
Collection
TinyML Model
Probabilities
[Wing, Ring, No Gesture]
Accelerometer data
200 * [ax, ay, az]
The gesture with the highest
probability could be
considered the detected one

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Color feedback
Deployment
Model
Testing
Model
Training
Problem
Definition
Data
Collection
Blue
Waiting for significant gesture
Off
Inferencing
in progress (2 seconds)
Green
Wing gesture detected
Yellow
Ring gesture detected

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Challenges and Future 🔮

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Challenges in Edge AI Development
🤩
Real-time processing
Lower latency
Reduced bandwidth usage
😰
Limited resources (RAM, CPU)
Trade-off between power & performance
Difficult model optimization

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Future of Edge AI
⚡ Increased Power on MCUs
Advances in microcontroller capabilities and the emergence of specialized
controllers (NPU, VPU, ...)
⚙ Optimized Algorithms
Continuous improvements in algorithms and the development of new
frameworks
🚀 New Applications
Emerging sectors, including smart wearables, drones, Industry 4.0, and more

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“Future” of Edge AI 󰣻
Source:
tefal.com.sg/Cooking-appliances/Rice-Cookers/RK736B-EASY-RICE-PLUS-spherical-pot-rice-cooker-1-8L/p/7211004623

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References
Arduino Nano Matter
https://docs.arduino.cc/hardware/nano-matter/
ML Magic Wand with Arduino Nano Matter
https://docs.arduino.cc/tutorials/nano-matter/ml-magic-wand/
Google AI LiteRT (former TensorFlow Lite)
https://ai.google.dev/edge/litert

44. Thank you! 🫶
Trento 󰏢, April 9th, 2025
Leonardo Cavagnis
✉ l.cavagnis@arduino.cc
🌎 leonardocavagnis.github.io
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