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.
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.
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.
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.
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 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.
On-Device Neural End-to-End Speech Recognition and Synthesis Algorithms - A Review
Comprehensive review of on-device neural speech recognition and synthesis algorithms, exploring end-to-end models, optimization techniques, and their potential for on-device implementation.
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.
Binarized Neural Networks on Microcontrollers
Explore Binarized Neural Networks for efficient deep learning on microcontrollers, enabling complex tasks with minimal memory and high performance using 1-bit encoding for weights and activations.
Security of Edge AI Against Hardware Attacks
Explore security challenges and countermeasures for Edge AI against hardware attacks, focusing on neural network protection and side-channel analysis in embedded systems.
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.
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.
A Technique for Extreme Compression of LSTM Models
Explore extreme LSTM compression using sparse structured additive matrices. Learn doping techniques, co-matrix adaptation, and regularization for state-of-the-art accuracy at high compression factors in NLP applications.
Pushing the Limits of RNN Compression Using Kronecker Products
Explore Kronecker Products for compressing sequence-based neural networks, achieving high compression factors for IoT RNN applications and addressing accuracy loss in large NLP tasks through doped Kronecker products.
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.
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.
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.
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.
Generative Neural Scene Representations for 3D-Aware Image Synthesis
Explore 3D-aware image synthesis using generative neural scene representations, covering GRAF, GIRAFFE, and CAMPARI models for advanced computer vision applications.