AI Model Efficiency Toolkit (AIMET) - Lecture 25
Explore AI model efficiency techniques with Qualcomm AI Research. Learn about AIMET, community research, core technologies, and industry applications for optimizing deep learning models.
U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power AIoT
Explore U-TOE, a universal toolkit for evaluating TinyML on low-power AIoT devices, presented at RIOT Summit 2023.
From Arduinos to LLMs: Exploring the Spectrum of ML - MLOps Podcast Episode 162
Explore MLOps spectrum from LLMs to TinyML. Gain insights on model deployment challenges, IoT integration, and strategies for constrained environments. Learn about the intersection of machine learning and electronics.
DavinSy Hands-on - Continuous Learning Beyond the Edge
Explore DavinSy's AI system for solving TinyML challenges, enabling training on Arm-powered embedded devices using real-time data. Learn fast prototyping and deployment techniques for Arm development boards.
Deep Dive - Unpacking the TinyML Cookbook
Explore practical TinyML applications and techniques with expert Gian Marco Iodice, gaining insights into efficient machine learning on resource-constrained devices.
Taking Control of Edge AI Projects - Tools and Automation
Explore the need for improved tools and automation in edge AI projects, featuring insights on SensiML's Piccolo AI™ open-source suite for TinyML development.
TinyML: From Concept to Reality - Transforming Everyday Devices
Explore TinyML's impact on smart devices, from smartphones to autonomous vehicles. Learn about AI applications in home automation, industrial settings, and the challenges of real-time analytics implementation.
Distributed Training and Gradient Compression - Part I - Lecture 13
Explore distributed training of neural networks, focusing on data parallelism and model parallelism techniques for efficient machine learning on resource-constrained devices.
Distributed Training and Gradient Compression - Part I - Lecture 13
Explore distributed training of neural networks, focusing on data parallelism and model parallelism techniques for efficient machine learning on resource-constrained devices.
MCUNet: Tiny Neural Network Design for Microcontrollers - Lecture 11
Explore algorithm and system co-design for tiny neural network inference on microcontrollers, focusing on TinyML, TinyNAS, and TinyEngine for efficient deep learning applications.
MCUNet: Tiny Neural Network Design for Microcontrollers - Lecture 11
Explore algorithm and system co-design for tiny neural network inference on microcontrollers, focusing on TinyML, TinyNAS, and TinyEngine for efficient deep learning applications.
Neural Architecture Search (Part II) - Lecture 8
Explore neural architecture search techniques, including hardware-aware approaches and joint optimization of hardware and neural architectures. Learn applications and advancements in efficient deep learning design.
Quantization in Neural Networks - Lecture 5
Explore neural network quantization, including numeric data types, K-means-based quantization, and linear quantization for efficient deep learning on resource-constrained devices.
Neural Architecture Search for Efficient Deep Learning - Lecture 9
Explore advanced neural architecture search techniques for efficient deep learning on resource-constrained devices. Gain insights into model optimization for mobile and IoT applications.
Neural Architecture Search (Part II) - Lecture 8
Explore neural architecture search techniques, including hardware-aware approaches and joint optimization of hardware and neural architectures. Learn applications and advancements in efficient deep learning design.
Ireland's Ubotica - Pioneering Edge-Based Earth Observation Imagery Delivery
Transforming Earth Observation: Ubotica's edge-based model delivers imagery updates to user handsets in minutes, revolutionizing the field with faster, more efficient data consumption.
EfficientML.ai: Introduction to Efficient Machine Learning - Lecture 1
Explore efficient machine learning techniques and their applications in this comprehensive introduction to EfficientML.ai from MIT's Prof. Song Han.
EfficientML.ai: Introduction to Efficient Machine Learning - Lecture 1
Explore efficient machine learning techniques and their applications in this introductory lecture from MIT's EfficientML course, led by Prof. Song Han.
Noise-Robust Quantum Machine Learning - Lecture 23
Explore noise-robust quantum machine learning techniques for efficient deep learning applications on resource-constrained devices, including quantum computing implementations.
Quantum Machine Learning - Lecture 22
Explore quantum machine learning fundamentals, bridging quantum computing and AI. Gain insights into this emerging field's potential to revolutionize computational approaches.
Efficient Transformers - Lecture 20
Explore efficient transformer architectures for optimized deep learning on resource-constrained devices. Learn techniques to enhance performance and reduce computational demands.