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Raven-Strike® Rugged Embedded Computer

Raven-Strike® is a fully rugged mission computer purpose-built for modern fighting vehicles. Featuring state of the art Intel® Xeon® Scalable Processor CPU and NVIDIA GPGPU high performance parallel processing, HD full motion video capture and encode, and complete sensor integration and data fusion, Raven-Strike® offers peerless capability in a SWaP optimized system.

Raven-Strike integrates maximum compute and networking throughout and enables deep learning and artificial intelligence capability through the use of neutral networks. Systel's advanced thermal management engineering ensures survival in extreme operating conditions.

Raven-Strike is a fully rugged system, built to exceed the harshest military specifications including shock and vibration, dust and water ingress, and EMI/EMC.




  • Dual Intel® Xeon® Scalable Processors
  • Latest NVIDIA® GPGPU
  • Full HD Video Capture and Encode
  • Dual 10GbE Fiber High Bandwidth Networking
  • PCIe/104 Expansion
  • Fully Sealed
  • Ultra Dense I/O Capability
  • Optimized for Major Battle Management Systems


  • -40° to 50°C Operating Temperature
  • MIL-STD-810G Shock and Vibration
  • MIL-STD-1275 Vehicle Power
  • IP66 Dust/Water Ingress
  • Def Stan 00-82
  • STANAG 4609
  • GVA

Raven-Strike® Workflow

Combat Vehicle Workflow workflow

Combat Vehicle Workflow

Raven-Strike™ is a single, multipurpose embedded mission computer, acting as a SWaP-2C optimized, single LRU system for all sensor ingest and data fusion. Raven-Strike combines high performance parallel CPU and GPU processing, high-bandwidth 10GbE networking, and storage functionality required for real-time sensor processing, video and sensor analytics, motion target processing, battle software management, real-time data exploitation and nextwork distribution. Raven-Strike makes possible manned-unmanned teaming (MUM-T) to offset information overload challenges for the warfighter by leveraging advanced computing technologies to deliver artificial intelligence and deep learning capabilities.

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