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.
Streamline Deep Learning for Video Analytics with DeepStream SDK 2.0
Explore AI-based video analytics using DeepStream SDK 2.0 for Tesla, transforming video into insights for smart cities with a modular plugin architecture and scalable framework for multi-stream processing and application development.
TensorFlow Models Accelerated for NVIDIA Jetson
Accelerate TensorFlow models on NVIDIA Jetson: optimize training, integrate TensorFlow RT, enhance image classification, and format models for improved performance.
Applied Deep Learning in Speech Recognition and Collaborative Filtering - Session 5
Explore cutting-edge AI applications in speech recognition and collaborative filtering systems, featuring NVIDIA Research insights from NeurIPS 2018.
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.
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.
Improving Energy Efficiency and Robustness of tinyML Computer Vision Using Log-Gradient Input Images
Explore innovative techniques to enhance energy efficiency and robustness in tinyML computer vision, focusing on log-gradient input images and architecture optimization for microcontrollers.
TinyML Talks - Oculi Is Putting the Human Eye in A.I.
Innovative imaging technology mimicking human vision for efficient machine perception, combining sensing and pre-processing on a single chip for superior energy-delay performance in AI applications.
Lightweight Face Detection for Ultra-Low Power AI Processors - tinyML Asia 2021
Lightweight face detection method for ultra-low power AI processors, featuring real-time application and accuracy in various scenarios. Explores implementation on Himax WE-I Plus platform for always-on sensing products.
An Approach to Dynamically Integrate Heterogeneous AI Components in Multimodal User Authentication - tinyML Asia 2021
Approach to dynamically integrate heterogeneous AI components for multimodal user authentication, focusing on flexibility, performance, and energy efficiency in battery-powered devices.
Putting AI on a Diet: TinyML and Efficient Deep Learning
Efficient deep learning techniques for deploying AI on resource-constrained devices, including model compression, neural architecture search, and new design primitives for various applications.
AIfES - An Open-Source Standalone AI Framework for Almost Any Hardware
Explore AIfES, an open-source AI framework for embedded systems, enabling neural network deployment and training on various hardware, including 8-bit microcontrollers and Arduino boards.
Acceleration of Deep Learning Inference on Raspberry Pi's VideoCore GPU
Explore techniques for accelerating deep learning inference on Raspberry Pi's VideoCore GPU, including optimized tools, libraries, and compilers for enhanced performance without compromising accuracy.
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.
Training Neural Networks for Sensors
Overview of training neural networks for gesture recognition using accelerometer data on a low-power device. Covers data collection, augmentation, network architecture, and performance for smartwatch applications.
Low Power CV Meets the Real World
Explore practical considerations for implementing computer vision in low-power IoT devices, covering hardware accelerators, algorithms, optics, and real-world challenges.
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.
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.
Driving with Attention
Explore innovative sensor fusion techniques for autonomous driving, focusing on TransFuser and NEAT models that leverage attention mechanisms to improve perception and decision-making in complex traffic scenarios.
Towards Animatable Human Avatars
Explore cutting-edge techniques for creating animatable human avatars using neural implicit shapes and meta-learning approaches, with applications in computer vision and graphics.