An Optimized Core Distribution Adaptive Topology Reconfiguration Algorithm for NoC-Based Embedded Systems
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View abstract on 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.
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.
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