KORTHOS / ECOSYSTEM / PRODUCT

DEEPX DX-V3 Series

About

About DEEPX DX-V3 Series

DEEPX’s DX-V3 architecture runs neural-network inference inside cameras, robots and embedded equipment with constrained power and thermal budgets. The processors combine neural-processing units, memory architecture and host interfaces for vision, detection, classification and other edge-AI workloads without continuous cloud execution.

Compiler, runtime, model-conversion and optimization tools translate frameworks including PyTorch and TensorFlow into deployable binaries. Customers can integrate packaged processors or modules to fit their throughput and system requirements. Applications span smart cameras, factory equipment, mobile robots, vehicles and security systems requiring real-time local processing and reduced network bandwidth.

Timeline

  • DEEPX unveils DX-M1M and DX-AIPlayer mass-production product lines at Embedded World 2026

    2026-03-10 - Product Launch - DEEPX introduced the mass-produced DX-M1M M.2 module and the DX-AIPlayer integrated edge-AI acceleration platform at Embedded World 2026. The company said the products were designed to simplify deployment of intelligent vision AI in industrial environments including robotics, smart factories, and automated logistics. DEEPX positioned the launches as part of its push to become a physical-AI hardware standard in Europe through immediate commercial deployment paths.

    Source

Context

Market context

DEEPX Edge AI Chip is the DX-series low-power inference accelerator family for robots, cameras and embedded edge devices.

  • Target environment: Embedded robotics systems, smart cameras, AIoT devices, industrial vision hardware and edge-compute modules.
  • Workflow context: Local AI inference, vision processing, perception workloads and power-constrained embedded compute.
  • Customer context: Robot builders, camera manufacturers, edge-device makers and developers needing on-device inference instead of cloud processing.
  • Deployment model: Commercial chip and module family supplied as an embedded AI compute component.
  • Commercial maturity: Commercial edge-AI hardware line with published product materials and performance positioning.
  • Adoption constraints: Adoption depends on software toolchain support, model compatibility, thermal design, module availability, integration effort and production supply.
  • Market position: Robot-stack compute component for local perception and inference where power and latency budgets matter.
  • Adjacent products: NVIDIA Jetson Orin, Ambarella CVflow, Hailo-8, Qualcomm RB3 Gen 2

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