Energy Observability Using Kepler - Revolutionizing Cloud Efficiency
Explore Kepler, an innovative framework for monitoring power consumption in Kubernetes environments, enhancing cloud efficiency through advanced observability techniques and machine learning.
Power Efficiency of Modern Servers
Explore power distribution in servers, analyze efficiency improvements, and examine the impact of inlet temperature changes on data center energy consumption.
Revolutionizing Sustainability: AI-Driven Disassembly
Explore AI-driven disassembly's role in sustainability, covering efficient methods, robotics integration, environmental impact, challenges, and future prospects.
The Role of Frontend Code in Reducing Carbon Emissions
Explore strategies for reducing carbon emissions through frontend code optimization, covering sustainable web design principles and practical techniques for eco-friendly development.
AI Disassembly for a Sustainable Circular Economy
Explore AI-driven disassembly techniques for sustainable product lifecycles, enhancing circular economy practices through innovative robotics and efficient assembly methods.
Balancing Speed and Sustainability - DevOps Strategies for Net Zero
Discover practical strategies to reduce IT carbon emissions while maintaining DevOps efficiency, from optimizing cloud infrastructure to identifying genuine sustainability initiatives and avoiding greenwashing.
Software Engineering at Google
Explore Google's software engineering principles: time, scale, and tradeoffs. Learn sustainable practices for maintaining large codebases and managing change in long-term projects.
Time Series Forecasting with XGBoost - Use Python and Machine Learning to Predict Energy Consumption
Learn to predict energy consumption using XGBoost and Python in this hands-on tutorial. Covers data preparation, feature engineering, model building, and forecasting techniques for time series analysis.
Machine Learning Based on Stochastic Magnetic Tunnel Junctions
Explore spintronics-based neuromorphic systems for efficient machine learning, addressing device variability through in situ learning in Boltzmann machines for autonomous AI applications.
Building Low-power Software for Nano-satellites
Explore challenges and solutions in developing software for nanosatellites, including disconnected operations, energy management, and communication issues in space environments.
Software-Driven TinyML Hardware Co-Design for AI Power Smart Devices
Explore software-hardware co-design for efficient TinyML systems, focusing on processing-in-memory accelerators to reduce power consumption and enhance AI inference in smart devices.
In-memory Computing and Dynamic Vision Sensors for Internet of Video Things
Exploring neuromorphic technologies, dynamic vision sensors, and in-memory computing for energy-efficient IoT video applications, with focus on innovative solutions for intelligent transportation and unattended ground sensors.
Neuromorphic Engineering Algorithms for Edge ML and Spiking Neural Networks
Explore cutting-edge neuromorphic engineering algorithms for edge ML, including hardware-friendly learning, spike-based intelligence, surrogate gradients, and meta-learning techniques.
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.
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.
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.