Enabling Neural Network at the Low Power Edge - A Neural Network Compiler
Explore neural network deployment in IoT devices, addressing challenges and optimizing for ultra-low power operation through hardware-software co-optimization and automatic code generation.
Powering Innovation in AI Devices on the Edge with Micro-NPUs - tinyML Asia 2020
Explore Arm's Ethos-U micro-NPUs for efficient AI processing on edge devices, enabling new applications and capabilities in embedded systems through advanced Machine Learning solutions.
Tiny but Powerful: Hardware for High Performance, Low Power Machine Learning
Explore cutting-edge hardware for ultra-low power machine learning at the edge, covering TinyML applications, model optimization, and emerging trends in efficient computing.
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.
Towards Software-Defined Imaging - Adaptive Video Subsampling for Energy-Efficient Object Tracking
Explore adaptive video subsampling for energy-efficient object tracking using software-defined imaging algorithms and CMOS sensors' ROI capabilities, with FPGA-accelerated implementations.
Recent Progress on TinyML Technologies and Always-On Machine Opportunities
Explore recent advancements in tinyML technologies, their implementation, and potential applications across various industries, with a focus on always-on machine learning for voice, vision, and sensing.
How to Train and Deploy TinyML Models for Three Common Sensor Types
Learn to train and deploy tinyML models for sensor, audio, and vision data using Edge Impulse. Explore workflows, best practices, and optimization techniques for embedded machine learning applications.
TinyML Talks - Low-Power Computer Vision
Explore energy-efficient computer vision techniques for battery-powered systems, including quantization, network architecture search, and knowledge distillation. Learn about hierarchical neural networks and the IEEE Low-Power Computer Vision Challenge.
Everything You Need to Know About Bluetooth Low Energy Advertising
Comprehensive guide to Bluetooth Low Energy advertising, covering basics, types, data formats, extensions, and practical implementation using nRF Connect SDK and nRF Sniffer.
Meet the Engineers: Exploring the Raspberry Pi Pico and RP2040 - Part 1
Explore the Raspberry Pi Pico and RP2040 with industry leaders in this engaging webinar, covering technical aspects, design choices, and potential applications of these innovative microcontrollers.
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.
From Small to Tiny: Co-designing ML Models, Computational Precision, and Circuits - Energy-Accuracy Trade-offs
Explore co-designing ML models, computational precision, and circuits for energy-efficient tinyML systems. Learn about cross-layer optimization and cascaded networks for embedded applications.
Visual AI in Milliwatts - A Grand Challenge and Massive Opportunity
Explore visual AI's potential in low-power devices, discussing challenges, opportunities, and applications in security cameras and beyond. Learn about deep learning, processors, and algorithms driving this technology.
Ultra-Low-Power Command Recognition for Ubiquitous Devices
Explore ultra-low-power command recognition for ubiquitous devices, covering noise challenges, system goals, core functions, training, and implementation on tiny hardware.
tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems
Explore benchmarking for ultra-low power ML systems, focusing on tinyMLPerf's methodology, tasks, and ecosystem impact. Learn about quantization and community-driven efforts in this emerging field.
Resource Efficient Machine Learning in a Few KBs of RAM - tinyML Summit 2020
Explore resource-efficient machine learning techniques for IoT devices with limited RAM, focusing on Microsoft's EdgeML library and algorithms like ProtoNN and FastRNN for edge computing.
Raspberry Pi Pico and Bluetooth
Learn to integrate Bluetooth with Raspberry Pi Pico using MicroPython. Covers UART, serial communications, and Bluetooth modules for enhanced project connectivity and control.
The "Top" Ten tinyML Commandments
Explore ten essential principles for tinyML success, covering data engineering, codesign, metrics, reproducibility, and industry collaboration, to drive innovation in embedded machine learning.
Avoiding Loss of Quality in Tiny Models - Neuton.ai Partner Session
Explore compact AI models without sacrificing quality. Learn to assess model quality, understand decision-making logic, evaluate training data, and interpret model outputs for tinyML applications.
tinyML Vision Challenge - Intel-Luxonis DepthAI Platform Overview
Explore Intel and Luxonis' DepthAI platform for advanced vision solutions. Learn about AI inference, OpenVINO toolkit, and developing spatial edge applications using the LUX-ESP32 device.
TinyML Summit 2021 Tiny Talks - TinyML Software Runtime for Hybrid Multicore Architecture
Explore software runtime for hybrid multicore architecture in TinyML, focusing on efficient resource allocation, minimized processing overhead, and optimized memory transfers for edge AI hardware.