Discovering and Managing IoT Devices from Kubernetes with Akri
Explore IoT device discovery and management in Kubernetes using Akri, enhancing edge computing capabilities and streamlining device integration for efficient deployment.
Observing Fastly's Network at Scale with Kubernetes and Strimzi
Explore Fastly's Autopilot system for optimizing edge network traffic, focusing on telemetry pipeline, k8s-based platform, and key operators like Strimzi Kafka. Learn about challenges in network-related use cases.
Kubernetes IoT Edge Working Group: Edge Device Onboarding and Management
Explore open-source tools for edge device lifecycle management, from secure onboarding to production use. Learn about CNCF, LF Edge, and Eclipse projects extending cloud capabilities to edge devices and I/O.
Energy Efficient Placement of Edge Workloads
Explore Kepler for container-level energy metrics in Kubernetes, enabling efficient workload placement and sustainable cluster management through integration with observability tools.
The Amazing Trace: Enhancing Distributed WebAssembly with OTEL
Explore how OTEL tracing enhances distributed WebAssembly in wasmCloud, improving observability and debugging for developers in complex distributed systems.
KubeVirt v0.53 - New Features for Durability, Security, and Pluggability
Explore KubeVirt v0.53's enhancements in scalability, security, storage, and networking. Learn about new features for high-performance computing, unprivileged VMs, and improved automation for cloud-native virtualization.
Overview of Challenges and Solutions for Orchestrating Applications to Multiple Data Centers and Edge Clusters
Explore challenges and solutions for orchestrating applications across multiple data centers and edge clusters, focusing on automation and zero-touch deployments.
Extending Kubernetes: Application Orchestration to Thousands of Clusters in Cloud and Edge
Explore Walmart's innovative approach to managing multi-tenancy and application deployment across thousands of Kubernetes clusters in cloud and edge environments using Helm and custom extensions.
AI on the Edge: Hardware-Software Co-Design for Graph Analytics and Neural Networks - Session 3
Explore cutting-edge research on hardware-software co-design for AI, including graph analytics, neural networks, object detection, and federated learning.
AI on the Edge - TinyML and Machine Learning for Edge Devices - Session 1
Explore cutting-edge research on TinyML and edge AI, covering neural navigation, IoT deep learning, distributed transformers, and collaborative edge training.
Introduction to MLOps at the Edge
Explore MLOps at the edge: benefits, challenges, and real-world applications. Learn about edge computing's advantages over cloud and its synergy with AI and cloud technologies.
On-Device Training Under 256KB Memory
Discover cutting-edge techniques for on-device machine learning training with minimal memory requirements, presented by MIT's Song Han in this technical seminar on resource-efficient AI deployment.
Querying Decentralized Data in Rhizomatic Systems
Explore Rhizome, a decentralized database for InterPlanetary Linked Data, and learn how it empowers developers to build innovative peer-to-peer applications with enhanced consistency and privacy.
Fission Reactor: Dialog - A Far-Edge Database for Local-First Applications
Explore Dialog, a far-edge database for local-first applications and autonomous computing agents, enabling decentralized data management and offline-first functionality.
Introduction to Kube-VIP - Bare Metal Load Balancing for Kubernetes
Explore kube-vip for bare metal Kubernetes load balancing. Learn about Plunder for server provisioning and see practical demos of control plane HA and service load balancing.
Exploring Modern and Secure Operations of Kubernetes Clusters on the Edge
Explore modern Kubernetes edge deployment security and operations, covering secure boot, OS image download, TPM for remote attestation, deployment options, GitOps management, and edge-cloud data synchronization.
Power Efficient Neural Network Based Hardware Architecture for Biomedical Applications
Power-efficient neural network hardware architecture for biomedical applications, focusing on sleep apnea detection using ECG and SpO2 data. Introduces SABiNN technique for reduced power consumption and compact design while maintaining accuracy.
On-Device Continuous Event-Driven Deep Learning to Avoid Model Drift
Explore on-device continuous event-driven deep learning to prevent model drift in AIoT applications, focusing on real-time data adaptation without user intervention for industrial use cases.
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.
On-Device Learning Under 256KB Memory - Challenges and Solutions for IoT Devices
Explore on-device learning for IoT devices with limited memory. Learn techniques for efficient model adaptation, quantization-aware scaling, and sparse updates to enable continuous learning on tiny hardware.
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.