Artificial Intelligence Accelerates Your Multi-Camera Solution

Recent advances in embedded vision have evolved from passive video capturing devices to fully autonomous vision systems. Self-driving cars, drones, or autonomous guided robots require real-time parallel processing, low-latency, and in some cases low current consumption. Multiple camera modules provide surround view and sensor fusion improves the overall vision system, while artificial intelligence and machine learning herald tremendous improvements for recognition and learning tasks in autonomous vision systems.

Building autonomous vision systems grows increasingly challenging, requiring multi-disciplinary expertise in optics, image sensors, computer vision and deep learning. Selecting the right development platform and design methodology is crucial for a successful implementation. Avnet Silica offers embedded vision solutions bundled with tools and reference designs to minimize development time in creating autonomous vision systems.

Requirement of a multi-camera solution

  • High data rate handling multiple camera input
  • Low latency for real-time execution / fast reaction time
  • Low power consumption
  • Reliability and safety
  • Flexibility and reconfigurability
  • Intensive computer vision and machine learning


  • ADAS surround view
  • Autonomous driving
  • Electronic mirrors
  • In-cabin cameras
  • Machine vision
  • Security
  • Unmanned aerial vehicles
  • Viewing systems
  • Virtual and augmented reality (VR/AR)


Multi-Camera methodology


The high-resolution multi-camera solution consists of 3 hardware core building blocks: camera modules, a multi-sensor FMC module, and a multi-processor SoC or SOM on a carrier board.


Camera modules Multi-Camera FMC module Carrier card
Multi-Camera Avnet Multi-Camera FMC Module product image Xilinx Camera Card
Flowchart: From camera modules to Multi-Camera FMC module to Carrier Card
ON Semiconductor Maxim Integrated Maxim Integrated Xilinx


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