Implementing Real-time Vision AI Apps Using NVIDIA DeepStream SDK
Explore real-time video analysis with NVIDIA DeepStream SDK. Learn to build high-performance apps for IoT sensors, leveraging pre-trained models and optimized pipelines for various industries.
NVIDIA Tools for Training and Deploying Intelligent Vision Applications at the Edge
Discover tools for training, building, and deploying intelligent vision applications at the edge using NVIDIA's suite for video analytics pipelines and IoT devices.
DeepStream SDK - Accelerating Real-Time AI-Based Video and Image Analytics
Accelerate real-time AI-based video and image analytics with DeepStream SDK. Learn about flexible deployment, architecture, and applications in retail, smart cities, manufacturing, and logistics.
Deploy AI with AWS ML and IoT Services on Jetson Nano
Learn to deploy AI models on Jetson Nano using AWS ML and IoT services. Explore SageMaker, Greengrass, and real-time video processing for edge computing applications.
Inference and Quantization for AI - Session 3
Explore quantized inference, TensorRT 5, and TensorFlow integration for AI model optimization. Learn about NVIDIA's inference server and techniques to improve model efficiency and performance.
Dissecting a Low Power AI/ML Edge Application - Noise Suppression
Explore noise suppression in AI/ML edge applications, focusing on energy-efficient implementation for battery-powered devices and software frameworks for optimal user experience.
Tiny Models with Big Appetites: Cultivating the Perfect Data Diet for Computer Vision
Explore techniques for curating optimal training datasets and designing sampling strategies for tiny computer vision models, emphasizing data quality and model robustness in real-world environments.
TinyM2Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices
Explore a flexible multimodal learning framework for tiny devices, featuring depth separable layers and mixed precision quantization, with applications in COVID detection and object recognition.
Delta Keyword Transformer: Bringing Transformers to the Edge Through Dynamically Pruned Multi-Head Self-Attention
Explore cutting-edge techniques for optimizing Transformers in edge computing, focusing on the Delta Keyword Transformer and its dynamic pruning approach for efficient multi-head self-attention.
Accelerate ML Development With Cloud-Based Arm Cortex-M Models
Explore cloud-based Arm Cortex-M models for accelerated ML development, featuring Virtual Hardware integration, Python-scriptable peripherals, and TensorFlow optimizations.
Airborne Sound Maintenance in Remote Sites Using Low Power Federated Learning
Explore low-power federated learning for remote site maintenance using airborne sound, minimizing downtime and costs while adhering to GDPR regulations.
Extremely Low-Bit Quantization for Transformers - tinyML Asia 2021
Explore extremely low-bit quantization techniques for Transformers, focusing on mixed precision strategies to optimize deployment on resource-limited devices without compromising accuracy.
Embedded Machine Learning in the Real World
Explore embedded ML applications, challenges, and opportunities in real-world scenarios, focusing on tiny models, accelerated hardware, and practical tooling.
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.
Networks Within Networks - Novel CNN Design Space Exploration for Resource-Limited Devices
Explore novel CNN design for resource-limited devices using "network within network" paradigm. Learn automated method for compressing neural networks while preserving accuracy and meeting size constraints.
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.
Saving 95% of Your Edge Power with Sparsity to Enable TinyML
Explore sparsity techniques to drastically reduce power consumption in edge ML, enabling real-time processing for tinyML applications with the innovative GrAI Core architecture.
TinyML Talks - Dr. Dominic Binks- TinyML Doesn’t Need Big Data, It Needs Great Data
Explore challenges and solutions in obtaining quality audio data for tinyML sound recognition, focusing on data sources, collection methods, labeling strategies, and performance evaluation.
Train One Network and Specialize It for Efficient Deployment
Explore efficient neural network deployment across diverse devices using the Once-for-All network approach, which enables quick specialization without retraining and outperforms state-of-the-art methods on edge devices.
NRF9160 - Optimized for Ultra Low Power and Small Cellular IoT Applications
Explore nRF9160 SiP for ultra-low power cellular IoT: LTE-M/NB-IoT connectivity, small form factor, extended battery life, GPS capabilities, and edge computing potential for innovative IoT applications.