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Summary

This study introduces an embedded AI system for federated learning, utilizing multiple AI processors and a novel controller for efficient, secure, and flexible client-side data training. This approach reduces network bandwidth and enhances data security in AI applications.

Keywords:
AI processorcontrollerdistributed learningembedded systemfederated learningindependent operationparallel recognition

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Area of Science:

  • Artificial Intelligence
  • Computer Engineering
  • Embedded Systems

Background:

  • Centralized AI systems consume high network bandwidth and pose security risks due to extensive data processing on servers.
  • Federated learning offers a decentralized approach where clients train models locally, transmitting only results to reduce network load and enhance data security.
  • Resource-limited client systems in federated learning benefit from multi-processor architectures, necessitating a controller for efficient AI core management.

Purpose of the Study:

  • To propose a novel embedded AI system architecture designed for flexible federated learning applications.
  • To develop and implement a controller for arbitrating and managing multiple embedded AI processors within the federated learning system.
  • To validate the operational effectiveness and performance of the proposed system for diverse AI tasks.

Main Methods:

  • Designed a flexible embedded AI system architecture tailored for federated learning.
  • Developed a dedicated controller for managing multiple AI cores, implemented on a Field-Programmable Gate Array (FPGA).
  • Verified system operations using image and speech processing applications and evaluated performance via simulation.

Main Results:

  • The proposed embedded AI system architecture supports flexible composition with various AI cores.
  • The FPGA-implemented controller effectively arbitrates and utilizes multiple AI processors for federated learning tasks.
  • Demonstrated successful operation and verified performance for image and speech applications.

Conclusions:

  • The developed embedded AI system provides a flexible and efficient solution for federated learning on resource-constrained client devices.
  • The FPGA-based controller is crucial for managing multi-AI core systems, enhancing both network efficiency and data security.
  • The proposed system architecture shows promise for advancing the deployment of federated learning in diverse embedded applications.