Quantization Courses and Certifications

tinyML

Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Explore power-of-two quantization for efficient neural networks, focusing on low bitwidth and hardware compatibility. Learn about key problems, methods, and results in this cutting-edge research.

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

tinyML

TinyML Talks Germany - AutoFlow - An Open Source Framework to Automatically Implement Neural

Explore AutoFlow: an open-source framework for automating neural network implementation on embedded devices, covering model building, optimization, and deployment.

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

tinyML

AI at the Edge - Enabling Vision for Low-Power Devices

Explore AI's shift to edge devices, focusing on building efficient computer vision algorithms for battery-powered devices. Learn about data collection, continuous learning, and optimization techniques.

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

tinyML

Machine Learning at the Extreme Edge of Always-on Intelligent Sensor Networks

Explore ML optimization techniques for resource-constrained IoT sensor nodes, focusing on system, algorithm, architecture, and circuit levels to achieve significant area-power improvements.

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

tinyML

The Model Efficiency Pipeline: Enabling Deep Learning Inference at the Edge

Explore optimizing deep learning for edge devices through model efficiency techniques, including neural architecture search, pruning, quantization, and conditional processing.

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

tinyML

SWIS: Shared Weight Bit Sparsity for Efficient Neural Network Acceleration

Explore SWIS, a quantization framework for efficient neural network inference acceleration, offering improved performance and storage compression through weight decomposition and scheduling algorithms.

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

tinyML

hls4ml: An Open-Source Co-Design Workflow for Scientific Low-Power Machine Learning Devices

Explore hls4ml, an open-source workflow for implementing machine learning algorithms on FPGAs and ASICs, enabling real-time processing for scientific applications and accelerating discoveries.

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

tinyML

Many Shades of Acceleration - An Open TinyML Platform Perspective

Explore extreme edge AI, energy efficiency, and acceleration techniques for TinyML applications with insights from the PULP platform and RISC-V processors.

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

tinyML

Advanced Network Quantization and Compression Using AIMET - tinyML Summit 2021

Explore advanced network quantization and compression techniques using AIMET to optimize AI models for efficient deployment on end devices, addressing memory, compute, and energy constraints.

• YouTube
• 1 hour 34 minutes
• On-Demand
• Free Video

tinyML

Build Industrial-Grade TinyML Applications with Edge Impulse - Tutorial

Learn to build industrial-grade tinyML applications using Edge Impulse. Covers dataset collection, model design, training, optimization, and integration into embedded systems running on MCUs.

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

tinyML

Building a Magic Wand with TensorFlow Lite Micro - Tutorial

Learn to build an IMU-based gesture recognition model for Arduino, covering data gathering, training, and deployment using TensorFlow Lite Micro and CMSIS-NN library.

• YouTube
• 1 hour 33 minutes
• On-Demand
• Free Video

tinyML

Introduction to Optimization Algorithms to Compress Neural Networks

Explore optimization techniques for compressing neural networks, including pruning and quantization, to enable deployment on resource-constrained devices like smartphones.

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

tinyML

The Akida Neural Processor - Low Power CNN Inference and Learning

Explore Akida Neural Processor's low-power CNN inference and edge learning capabilities, featuring event-based computing and on-chip learning for visual and auditory applications.

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

tinyML

A Review of Compression Methods for Deep Convolutional Neural Networks

Explore compression techniques for Convolutional Neural Networks, including pruning, distillation, and quantization, to optimize deployment on tinyML devices without compromising accuracy.

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

tinyML

ELL: The Microsoft Embedded Learning Library - Principles and Applications

Explore Microsoft's Embedded Learning Library for efficient AI on resource-constrained devices. Learn about quantization, acceleration techniques, and architecture search for optimized machine learning deployment.

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