AI Edge Inference Computer

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Choose Your EDGEBoost Node

RCO-6000-CML AI Edge Inference Computer support modular add-on nodes for powerful inferencing capabilities and high-performance NVMe storage. Users can select specific nodes that provide high-speed NVMe storage, high-density SATA storage, and even GPU acceleration for machine learning and AI workloads.

10th Gen Intel® Core™ and Intel® Xeon® W Processors

The RCO-6000-CML AI Edge Inference Computer leverages rich performance enhancements provided by Intel 10th Generation Core and Xeon-W processors with a W480E chipset. The processors ensure powerful and reliable performance benchmarks amid the most computing-intensive applications for mission-critical data acquisition and telemetry in edge computing deployments.

  • 10th Gen Intel® Core™ (35W/65W TDP)
  • Up to ten hyperthreading cores
  • Intel® UHD Graphics 630
  • Intel® Xeon® W-1290TE (35W TDP)
  • Up to ten hyperthreading cores
  • Supports Error Correction Code (ECC) memory

Scalable and Versatile I/O

The RCO-6000-CML Series supports two universal I/O brackets to enable high-speed connections with low-latency data transmission for advanced industrial applications at the rugged edge. With diverse modular configurations, the computer’s I/O flexibility allows workload consolidation for handling various digital and analog sensors.

Remote and Wireless Connectivity from the Rugged Edge

The RCO-6000-CML Series enables seamless wireless connectivity for remote and mobile edge deployments. Systems feature high-end wireless technologies to reliably connect to sensors and network systems through a wireless IoT enterprise during remote and mobile edge deployments.

  • Wi-Fi 6
  • Bluetooth 5.0
  • Embedded Dual-SIM 4G/LTE cellular
  • Modular Dual-SIM 5G cellular

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Inference Analysis and Machine Learning

The RCO-6000-CML AI Edge Inference Computers supports GPUs (graphic processing units), allowing systems to process and analyze large volumes of complex data to drive AI functions at the edge. AI edge inference computers are purpose-built for deployment in either controlled or dynamic environments to capture data from sensors, high-resolution cameras, and other devices and peripherals that use real-time data to enable machine intelligence.

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Redundant IoT Data Storage

The base system can be configured with 3x 2.5” SATA SSDs, 1x internal 2.5” SATA SSDs at 9mm height, and 2x external hot-swappable SATA SSDs at 7mm height. The external 2.5” bays permit toolless, hot-swappable replacement of the SATA drives, simplifying service, and capacity upgrades. The addition of NVMe canister bricks enables high-speed read and write speeds for mission critical data acquisition.

  • High-Speed NVMe storage
  • High-Capacity SATA storage
  • Hardware and Software RAID Availalble

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Data Bricks for Data Acquisition

Toolless hot swappable canister bricks can be quickly swapped, providing easy data management and enterprise redundancy for applications that use data for machine learning. Two removable canisters can be populated with up to four hot swappable U.2 NVMe SSDs, providing organizations with high-speed, solid-state storage at the edge and the ability to load and offload canisters for extremely quick data transfers. SATA SSDs models are also available to support high-capacity storage requirements.

Storage Options Include:

  • Hot swappable NVMe SSD Cannister Bricks
  • Individual 2.5” Toolless NVMe Drive Trays in 7mm/15mm
  • High Capacity 2.5” Toolless 7mm/15mm SATA SSDs

Data Protection and Anti-Corruption

The EDGEBoost Nodes have Safety Storage Ejection Buttons for data protection while swapping the hot-swappable storage. Pressing the button suspends all I/O operations and read/write procedures to the storage devices to prevent the loss or corruption of data.

Active Cooling Where It Counts

When the RCO-6000-CML Series is equipped with an edge boost node, the enclosure housing the NVMe SSD storage drives and/ or GPU comes with a hot-swappable blower fan, delivering cooling where it counts.

Industrial PC Powers Automation and Intelligent Computing

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