TinyML Auto ML Deep Dive: GAPflow - From TFLite and ONNX to Optimized C Code
Explore GAPflow: transform neural networks into optimized C code for ultra-low-power GAP processors. Learn quantization, validation, and memory allocation for efficient AI deployment on energy-constrained devices.
TinyML Talks Germany - Neural Network Framework Using Emerging Technologies for Screening Diabetic
Innovative framework using neural networks and emerging technologies for efficient screening of Diabetic Retinopathy, achieving high accuracy and significant improvements in energy consumption and latency.
TinyML Talks South Africa - What Is TinyML?
Explore tinyML applications, tools, and resources in South Africa. Learn about this emerging technology's potential and engage in community discussions for future workshops and events.
Programmable In-Memory Computing Accelerator with 100 SRAM IMC Macros
Explore programmable in-memory computing accelerators for efficient deep neural network processing, featuring 100 SRAM IMC macros and innovative architecture design for improved energy efficiency.
Accelerating Vision AI Applications Using NVIDIA Transfer Learning Toolkit and Pre-Trained Models
Simplify vision AI model creation using NVIDIA's Transfer Learning Toolkit and pre-trained models. Accelerate development with zero-coding tools, fine-tuning options, and optimization techniques for high-performance inference across various industries.
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.
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.
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.
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.
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.
Energy-efficient On-device Processing for Next-generation Endpoint ML - tinyML Summit 2020
Explore energy-efficient on-device processing for endpoint machine learning, focusing on Arm's Cortex-M55 CPU and Ethos-U55 NPU for next-generation AI 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.
SWIS: Shared Weight Bit Sparsity for Efficient Neural Network Acceleration
Explore SWIS, a quantization framework for efficient neural network inference acceleration, offering improved performance and storage compression through weight decomposition and scheduling algorithms.
tinyML: Impact and Growth of Always-On Devices
Explore the growing impact of tinyML devices in everyday life, from always-on voice in phones to environmental sensing in machines, and learn about the market's rapid expansion and technological requirements.
TinyML for Good: Ready to Take Off for a Big Impact
Explore tinyML's potential for positive impact through expert insights on vision, value, device recommendations, education, and involvement opportunities.
Why It Pays to Study Psychology - Lessons from Computer Vision
Explore how psychology insights enhance computer vision, from texture analysis to scene recognition and 3D reasoning. Learn about occlusion, visual memory, and self-supervised learning.
Quantum Computing with Just 1 Qubit - The Elitzur-Vaidman Bomb Algorithm
Explore quantum computing fundamentals and the Elitzur-Vaidman bomb detection algorithm using a single qubit, covering key concepts and practical applications in this engaging lecture.
Tracking Techniques in Computer Vision - Session 25
Explore advanced tracking techniques and algorithms for object detection and motion analysis in computer vision applications.
Kubernetes Security and Governance Made Easy Using Otomi
Discover how Otomi simplifies Kubernetes security and governance, leveraging open-source projects to enhance container and network security policies through an intuitive web interface, reducing misconfigurations and attack surfaces.
Propagating Programming Paradigms - Lifting High Performance Compute Into Kubernetes
Explore how to optimize Kubernetes clusters for high-performance computing workloads, bridging the gap between traditional HPC and cloud-native environments to improve performance and sustainability.