tinyML Courses

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.

• YouTube
• 27 minutes
• On-Demand
• Free Video

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.

• YouTube
• 28 minutes
• On-Demand
• Free Video

In-memory Computing and Dynamic Vision Sensors for Internet of Video Things

Exploring neuromorphic technologies, dynamic vision sensors, and in-memory computing for energy-efficient IoT video applications, with focus on innovative solutions for intelligent transportation and unattended ground sensors.

• YouTube
• 32 minutes
• On-Demand
• Free Video

TinyML Talks - Low-Cost Energy-Aware Sensor Data Acquisition at Scale

Explore energy-efficient sensor data acquisition at scale, focusing on low-cost connectivity solutions and best practices for device-to-cloud time series replication in various industries.

• YouTube
• 49 minutes
• On-Demand
• Free Video

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.

• YouTube
• 21 minutes
• On-Demand
• Free Video

Continual On-Device Learning on Multi-Core RISC-V Microcontrollers

Explore continual on-device learning for multi-core RISC-V microcontrollers, focusing on system architecture, memory-energy-accuracy trade-offs, and efficient implementation of backpropagation techniques using latent replays.

• YouTube
• 25 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

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.

• YouTube
• 20 minutes
• On-Demand
• Free Video

Autonomous Nano-UAVs: An Extreme Edge Computing Case

Exploring autonomous nano-UAVs as extreme edge computing cases, focusing on onboard intelligence, machine learning techniques, and full-stack solutions for navigation and human-robot interaction in resource-constrained environments.

• YouTube
• 31 minutes
• On-Demand
• Free Video

TinyML AutoML Tutorial: Building Production-ready Models with Imagimob AI

Explore Imagimob AI platform for creating production-ready ANN models for time-series data on resource-constrained devices. Learn data visualization, preprocessing, model training, and evaluation techniques.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Neuromorphic Engineering Algorithms for Edge ML and Spiking Neural Networks

Explore cutting-edge neuromorphic engineering algorithms for edge ML, including hardware-friendly learning, spike-based intelligence, surrogate gradients, and meta-learning techniques.

• YouTube
• 40 minutes
• On-Demand
• Free Video

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.

• YouTube
• 29 minutes
• On-Demand
• Free Video

TinyML Auto ML Deep Dive: GAPflow - From TFLite and ONNX to Optimized C Code

Explore GAPflow: transform neural networks into optimized C code for ultra-low-power GAP processors. Learn quantization, validation, and memory allocation for efficient AI deployment on energy-constrained devices.

• YouTube
• 40 minutes
• On-Demand
• Free Video

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.

• YouTube
• 1 hour
• On-Demand
• Free Video

Enabling On-Device Learning at Scale

Explore on-device learning techniques for AI personalization, including few-shot learning, continuous learning, and federated learning, addressing privacy and efficiency challenges in edge computing.

• YouTube
• 37 minutes
• On-Demand
• Free Video