Industry 4.0 is driving the demand for AI at the edge of Industrial IoT. Edge computing brings data processing, analysis, and storage closer to the source of data, which reduces latency, frees up bandwidth for mission critical applications, and provides real time data insights for decision making. AI enhanced edge computing further accelerates productivity and efficiency by facilitating real time problem solving and process optimization with data intelligence. To get the most out of edge AI deployments, adopting a robust hardware and software strategy is key.
On September 9th, 2021, join us on Embedded Computing Design’s A.I. Day webcast from 11:00am to 4:30pm ET where industry experts including Dustin Seetoo, Director of Product Marketing here at Premio, will provide thought leadership on deploying AI systems at the edge of IoT. We hope to see you there!
AI and edge computing has proven to increase productivity and efficiency by providing real-time data intelligence which enables faster and more accurate decisions and actions. To get the most out of edge AI deployments, a holistic hardware strategy is fundamental to support powerful software algorithms. Follow the link for a complete list of topics and speakers who will provide valuable insights on hardware and software requirements for a successful edge AI deployment.
AI Inference at the edge
Deploying powerful inference computers at the edge enables organizations to reflexively respond to situational data, glean keener insights into their operations, achieve low latency data processing for decision-making, and reduce network demands.
Running AI inferencing algorithms requires lots of compute power. Premio has combined next-generation processing and high-speed storage technologies with the latest IoT connectivity features to create a solution designed from the ground up to deliver holistic inference analysis at the rugged edge.
Premio's EDGEBoost Nodes
The 5G ready RCO-6100 Series incorporates advanced technologies such as Intel 9th Generation Core Processors, GPU support, and hot-swappable NVMe SSD storage. These features provide businesses with low latency data processing and critical compute power to run complex inference analysis at the edge.
The two-piece modular design provides multiple and flexible storage options and helps maintain the ruggedness of the industrial PC. The top RCO-6100 houses the CPU, motherboard, and sensitive electronics, and the bottom EDGEBoost Nodes provide NVMe SSDs and GPUs configuration options. Each EDGEBoost Node uses powerful high-RPM active cooling to prevent the system from overheating and ensure the longevity of sensitive components. This flexible modularity allows system integrators to quickly deploy the RCO-6100 AI Edge Inference computer in scale even with various application requirements in compute, storage, and connectivity.