FogHorn - Designed for Industrial IOT

FogHorn Products—Designed for Industrial IOT FogHorn brings a groundbreaking dimension to the Industrial Internet of Things (IIoT) by embedding built-in edge intelligence and fog computing directly into small footprint edge devices. At the core of Foghorn’s technology platform is the world’s most compact, advanced and feature rich complex event processor (CEP) which is revolutionizing IIoT. The software’s extremely low overhead allows it to be embedded into a broad range of edge devices and highly-constrained environments for maximum efficiency and speed. FogHorn’s Lightning™ product portfolio resides at the IIoT Edge, greatly enriching the capability of any gateway, programmable logic controller (PLC), industrial PC, HMI device, or other edge system and delivering unprecedented low latency for on site data processing, real time analytics, machine learning and AI capabilities. Our Newest Release: Lightning EdgeML™ Lightning EdgeML, our newest software release, introduces powerful machine learning capabilities to the edge. This release is included in both the Standard and Micro editions of FogHorn Lightning. Lightning Edge ML brings the power of machine learning to the edge in three groundbreaking ways: 1. Leverages existing models and algorithms: With Lightning Edge ML, industrial users can seamlessly plug in and execute proprietary algorithms and machine learning models on live data streams produced by physical assets and industrial control systems. 2. Makes machine learning OT-accessible: Non-technical personnel can use FogHorn’s tools to generate powerful machine learning insights without the need to rely on in-house or third party data scientists. 3. Runs in tiny software footprint: Lightning Edge ML enables complex machine learning models to run on highly-constrained compute devices such as PLCs, Raspberry Pi systems and tiny ruggedized IIoT gateways as well as more powerful industrial PCs and servers. Even with the addition of advanced machine learning capabilities, the complete Micro edition of the Lightning ML platform requires less than 256MB of memory footprint.