Edge Compute

Failover Failover is the transfer of service from a failed component or path to a standby or alternate one to preserve continuity during outages or faults. Operations Federated Learning Federated Learning is a distributed machine learning training approach in which a shared model is trained collaboratively across multiple decentralized devices or sites, each using its own local data, with… Concept/Framework Fog Computing A distributed compute architecture spreading processing, storage, and networking functions across a hierarchy of nodes positioned between the network edge and the centralized cloud, providing a more distributed, multi-tier alternative… Concept/Framework GPU GPU is a programmable hardware accelerator designed to perform many arithmetic operations in parallel, originally for graphics processing and now widely used for general-purpose compute and AI workloads. Hardware Grid-Edge Connectivity The network connectivity layer extending to distributed energy assets at the physical edge of the electrical grid — smart meters, inverters, EV chargers, and distribution automation devices — providing the… Concept/Framework IoT Gateway A hardware device positioned between constrained edge devices and backend network infrastructure, aggregating device traffic and translating between the device-facing protocol — LoRaWAN, NB-IoT, Modbus, and similar — and the… Hardware Machine Vision Machine Vision is the application of imaging hardware and image-processing or computer-vision software to automate industrial tasks such as inspection, defect detection, measurement, and robotic guidance, typically operating in real… Concept/Framework Mobile Edge Computing The original name for the architectural concept of hosting application compute close to the radio access network, later renamed to Multi-Access Edge Computing to correct the original name's implication of… Concept/Framework Model Quantization Model Quantization is the reduction of the numeric precision used to represent model weights, activations, or both, in order to improve inference efficiency, reduce memory footprint, and lower hardware requirements. Concept/Framework Model Serving Model Serving is the practice of deploying a trained machine learning or AI model within a hosting infrastructure — typically cloud-based — and exposing it through a network-accessible interface, such… Software