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Related Experiment Video

Updated: May 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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An Optimized Core Distribution Adaptive Topology Reconfiguration Algorithm for NoC-Based Embedded Systems.

Bowen Hou1, Dali Xu1, Fangfa Fu2

  • 1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.

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|April 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive algorithm for network-on-chip (NoC) topology reconfiguration, optimizing core distribution to improve system stability. The new method enhances reconfiguration success rates and significantly reduces recovery time in multicore embedded systems.

Keywords:
adaptive core distribution optimizationcore-level redundancynetwork-on-chiptopology reconfiguration

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Area of Science:

  • Computer Engineering
  • Embedded Systems
  • Network-on-Chip (NoC) Architectures

Background:

  • Multicore embedded systems rely on Network-on-Chip (NoC) for efficient core communication.
  • Increasing core counts elevate the risk of core failures, impacting system performance and stability.
  • Existing NoC topology reconfiguration algorithms struggle to balance reconfiguration speed and recovery time.

Purpose of the Study:

  • To propose an adaptive core distribution optimization topology reconfiguration algorithm for NoC systems.
  • To address the limitations of current algorithms in handling core failures and optimizing recovery.
  • To enhance the performance and reliability of multicore embedded systems facing core failures.

Main Methods:

  • Developed an adaptive algorithm focusing on faulty core distribution for reconfiguration.
  • Utilized a 2D REmesh structure for physical topology reconfiguration.
  • Employed a bidirectional search algorithm for optimization and an adaptive core distribution strategy.

Main Results:

  • Achieved a 96.70% successful reconfiguration rate with up to 68.75% faulty cores.
  • Demonstrated a 63.60% success rate with 8 faulty cores in an 8x9 REmesh, outperforming BTTR and BSTR.
  • Reduced average recovery time by 98.60% compared to BTTR and 15.87% compared to BSTR.

Conclusions:

  • The proposed adaptive algorithm significantly improves reconfiguration success rates and reduces recovery times in NoC systems.
  • This approach offers a superior balance between topology reconfiguration rate and recovery time.
  • The algorithm enhances overall system performance and reliability in advanced multicore embedded systems.