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Hardware-Software Partitioning for Real-Time Object Detection Using Dynamic Parameter Optimization.

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This study introduces a hybrid hardware-software approach to enhance real-time object detection. Artificial intelligence manages hardware components on FPGAs, improving computer vision algorithm performance and efficiency.

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Area of Science:

  • Computer Engineering
  • Artificial Intelligence
  • Embedded Systems

Background:

  • Real-time computer vision algorithms face challenges in memory bandwidth and energy consumption.
  • Current implementations in devices like smartphones and automotive systems require optimization.
  • Object detection is crucial for various monitoring and security applications.

Purpose of the Study:

  • To improve the quality of real-time object detection computer vision algorithms.
  • To propose a hybrid hardware-software implementation for enhanced performance.
  • To explore efficient allocation of algorithm components to hardware (IP Cores) and software.

Main Methods:

  • Developed a hybrid hardware-software implementation strategy.
  • Investigated methods for allocating algorithm components to hardware IP Cores.
  • Utilized embedded artificial intelligence for dynamic hardware configuration and parameter adjustment.
  • Implemented the solution on a Xilinx Zynq-7000 SoC FPGA demonstrator.

Main Results:

  • Demonstrated significant performance gains in object detection use-cases.
  • Showcased the benefits of hybrid hardware-software implementations.
  • Highlighted major improvements achieved by AI-managed IP Cores.
  • Validated the approach on a practical FPGA system.

Conclusions:

  • Hybrid hardware-software implementations offer substantial benefits for real-time computer vision.
  • AI-driven management of hardware IP Cores leads to major performance gains.
  • The proposed method effectively addresses challenges in memory bandwidth and energy consumption for object detection.