Speech-to-Intent on MCU: TinyML for Efficient Device Control - Lecture 6
Learn efficient speech-to-intent techniques for MCUs, focusing on direct parsing of user utterances into actionable output. Explore model training, deployment, and testing on Wio Terminal.
Wio Terminal TinyML Weather Prediction with TensorFlow Lite and LVGL GUI - Course 4
Create an intelligent meteostation using Wio Terminal and TensorFlow Lite for Microcontrollers to predict weather and precipitation based on local environmental sensor data, with LVGL GUI integration.
Wio Terminal TinyML Course - People Counting and Azure IoT Central Integration - Part 3
Learn to implement people counting using TinyML on Wio Terminal, integrating ultrasonic sensors and Azure IoT Central for real-time monitoring and data visualization.
Machine Learning Inference on Raspberry Pi Pico 2040 via Edge Impulse
Learn to implement machine learning inference on Raspberry Pico 2040 using Edge Impulse, covering environment setup, sample data inference, sensor data processing, and porting details.
Wio Terminal TinyML Course - Audio Scene Recognition on Microcontrollers with Arduino IDE
Learn to implement audio scene recognition on microcontrollers using TinyML, Arduino IDE, and Wio Terminal. Covers data collection, processing, model training, deployment, and mobile notifications with Blynk.
Introduction to TinyML on Wio Terminal - Machine Learning on Microcontrollers
Introduction to TinyML on Wio Terminal: Learn to implement machine learning on microcontrollers using Arduino IDE, covering data gathering, processing, model training, and deployment.
Power Efficient Neural Network Based Hardware Architecture for Biomedical Applications
Power-efficient neural network hardware architecture for biomedical applications, focusing on sleep apnea detection using ECG and SpO2 data. Introduces SABiNN technique for reduced power consumption and compact design while maintaining accuracy.
Software-Driven TinyML Hardware Co-Design for AI Power Smart Devices
Explore software-hardware co-design for efficient TinyML systems, focusing on processing-in-memory accelerators to reduce power consumption and enhance AI inference in smart devices.
On-Device Continuous Event-Driven Deep Learning to Avoid Model Drift
Explore on-device continuous event-driven deep learning to prevent model drift in AIoT applications, focusing on real-time data adaptation without user intervention for industrial use cases.
tinyML Trailblazers: Success Stories with Massimo Banzi - Arduino's Journey and Vision
Explore Arduino's journey, from its origins to its impact on machine learning and global tech education, with insights from co-founder Massimo Banzi on innovation and future directions.
Optimizing Data-Flow in Binary Neural Networks
Explore optimizing data flow in Binary Neural Networks, focusing on hardware efficiency, quantization techniques, and accuracy improvements for embedded systems.
Neuromorphic Intelligence and Learning in Robotics
Explore neuromorphic engineering for efficient AI in robotics, focusing on on-chip learning and processing algorithms using Intel's Loihi 2 chip and the Lava software framework.
TinyML Auto ML Deep Dive: Streaming Analytics for Edge Devices
Explore streaming analytics for edge devices with Stream Analyze's platform. Learn to develop, deploy, and monitor edge AI solutions using real-time data processing and on-device intelligence.
On-Device Learning Under 256KB Memory - Challenges and Solutions for IoT Devices
Explore on-device learning for IoT devices with limited memory. Learn techniques for efficient model adaptation, quantization-aware scaling, and sparse updates to enable continuous learning on tiny hardware.
tinyML Trailblazers: Success Stories with Mouna Elkhatib
Explore tinyML success with Mouna Elkhatib, AONDevices CEO. Learn about voice processing innovations, entrepreneurial challenges, and future trends in edge AI and IoT technologies.
tinyML Trailblazers - Success Stories in TinyML with Thierry Moreau
Explore tinyML success stories with Thierry Moreau, discussing OctoML's journey, Apache TVM, deployment challenges, and the future of edge AI in an interactive conversation.
Sensors and Machine Learning: Waking Smarter for Less
Explore how machine learning optimizes sensor systems, reducing resource usage while improving response. Learn about smart wake-up strategies and efficient data conversion at the edge.
Sensing Applications as a Driver for TinyML Solutions
Explore intelligent MEMS applications in motion learning, sports analytics, and environmental sensing. Learn how domain knowledge optimizes TinyML for ultra-low power contexts and shapes emerging ecosystems.
1 kB and Not a Bit More - The Ideal Weight for a TinyML Model
Explore automated creation of ultra-compact ML models for tiny smart devices, enabling efficient embedding in memory-constrained hardware without data science expertise.
Optimizing AutoML for the tinyML Future
Optimizing AutoML for tinyML: Balancing accuracy, latency, and power efficiency through hardware-aware techniques and full signal flow optimization from sensor to inference.
EON Tuner - AutoML for Constrained Devices in TinyML
Explore AutoML for constrained devices with EON Tuner, optimizing tinyML models through pre-processing, DSP, and ML combinations while considering device limitations and real-world performance.