Tinyml

Tinyml

Environmental Anomaly Detection with Microcontrollers and TinyML

Environmental anomaly detection brings together the power of TinyML and microcontrollers to monitor conditions like gas levels, temperature, and humidity in real time. Instead of relying on fixed thresholds or labeled fault data, these systems learn what “normal” looks like and flag unusual patterns that could signal a problem — from gas leaks to rapid temperature spikes. By using lightweight models such as autoencoders, you can deploy intelligent, unsupervised anomaly detection directly on low-power devices, making it possible to create proactive, autonomous monitoring solutions for industrial, agricultural, and environmental applications.

Read more 9 min read
Tinyml

Gesture Recognition with an Accelerometer using TinyML and ESP32 WROVER Kit

With a simple accelerometer and the ESP32 WROVER, TinyML can turn everyday movements into powerful commands. By collecting and labeling motion data, training a lightweight neural network, and running it directly on the microcontroller, this project brings intuitive gesture-based control to smart devices without relying on the cloud. From wearable tech to robotics, the possibilities for touchless, natural interaction are endless.

Read more 7 min read
Tinyml

Building a Smart Motion Detector with TinyML and ESP32 WROVER

Using TinyML on the ESP32 WROVER, a simple PIR sensor can become a smart motion detector that not only senses movement but classifies it in real time. By collecting diverse motion data, training a compact neural network, and deploying it efficiently on the ESP32, this system can distinguish between humans, pets, and background activity. With careful calibration, sensor placement, and optional Wi-Fi integration, it becomes a reliable, intelligent solution for home automation, security, and energy-saving applications.

Read more 5 min read
Tinyml

TinyML Projects with the ESP32 WROVER Kit and Common Arduino Sensors

The ESP32 WROVER kit, combined with common Arduino-compatible sensors, offers an accessible way to explore TinyML and bring real-time AI to embedded projects. From a simple voice-controlled RGB LED to advanced vision-based traffic detection, these project ideas progress in difficulty while introducing new skills such as audio preprocessing, sensor fusion, anomaly detection, and low-power optimization. By following this path, you’ll build a diverse portfolio of intelligent systems and gain hands-on experience in deploying machine learning models directly onto microcontrollers.

Read more 4 min read
Tinyml

Building a Voice-Controlled Toy Car with TinyML

A voice-controlled toy car is a practical and engaging way to explore TinyML. By training a lightweight machine learning model to recognize specific commands and deploying it to a microcontroller, you can make a car respond to your voice without cloud services or external modules. This guide walks through the complete process from data collection to deployment, showing how to integrate AI, embedded hardware, and robotics into one project.

Read more 4 min read
Tinyml

TinyML vs. Dedicated Voice Recognition Modules for Embedded Projects

Voice control in embedded systems can be implemented in two main ways: running a custom TinyML model directly on your microcontroller or using a dedicated voice recognition module. Each approach offers distinct advantages in flexibility, performance, and development effort. This post explores how both methods work, compares their strengths and limitations, and helps you decide which is best for your next project.

Read more 5 min read
Tinyml

Mastering the Training Phase in TinyML: Foundations for Embedded AI

TinyML is transforming the way AI interacts with everyday devices by enabling machine learning models to run directly on microcontrollers and other resource-constrained hardware. While much attention goes to deployment and inference, the training phase is where a model’s real capabilities are forged. Understanding the training process, from dataset preparation to optimization for embedded devices, is essential for building high-performance TinyML solutions.

Read more 5 min read
Tinyml

TinyML Software Stacks Overview: Tools for Running AI on Microcontrollers

TinyML is bringing artificial intelligence to the smallest of devices — microcontrollers with only kilobytes of RAM and ultra-low power budgets. At the heart of this movement are specialized software stacks that bridge the gap between cloud-trained models and the realities of embedded hardware. From TensorFlow Lite for Microcontrollers and Edge Impulse to CMSIS-NN and Nordic Semiconductor’s newly acquired Neuton platform, these tools provide the optimization, runtime efficiency, and hardware integration needed to run AI at the edge. This post explores the leading TinyML stacks, how they work, and how developers can choose and combine them for maximum impact.

Read more 6 min read
Tinyml

Understanding TinyML Inference on Resource-Constrained Devices

TinyML brings machine learning out of the cloud and into the smallest of devices, enabling real-time, low-power intelligence at the edge. At the heart of this capability lies inference — the process of turning raw sensor data into actionable insights directly on a microcontroller with kilobytes of RAM and milliwatts of power. This article explores how inference works on resource-constrained hardware, the optimizations that make it possible, and the challenges developers face when balancing accuracy, performance, and efficiency.

Read more 8 min read
Tinyml

What is TinyML and Why it Matters for Resource-Constrained Devices

TinyML is revolutionizing the way we think about artificial intelligence at the edge. Unlike traditional machine learning models that demand significant computational power, memory, and energy, TinyML focuses on running intelligent algorithms directly on resource-constrained devices—devices with less than 100 KB of RAM and power budgets under 50 mW. This breakthrough enables everyday gadgets like wearable health monitors, industrial sensors, and autonomous drones to make smart decisions locally, without relying on cloud connectivity. By bringing AI closer to the source of data, TinyML not only reduces latency and energy consumption but also enhances privacy and reliability, opening up a new frontier for real-time, on-device intelligence in the most constrained environments.

Read more 6 min read