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A Dynamic Reconfigurable Routing Framework for Efficient Data Packet Allocation in Network-on-Chip Architectures
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Keywords
Network-on-Chip
Adaptive Routing
Reconfigurable Architecture
Data Packet Allocation
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Abstract
The increasing complexity of multi-core and many-core systems has elevated the importance of efficient communication infrastructures, particularly Network-on-Chip (NoC) architectures. Traditional routing mechanisms often fail to adapt dynamically to varying traffic conditions, leading to congestion, latency degradation, and inefficient resource utilization. This paper proposes a dynamic reconfigurable routing framework designed to optimize data packet allocation in NoC architectures through adaptive decision-making mechanisms inspired by Artificial Intelligence and Machine Learning paradigms. The framework integrates traffic-aware routing, congestion prediction, and adaptive path reconfiguration to enhance performance under diverse workloads. By leveraging statistical modeling and adaptive control strategies, the proposed system dynamically adjusts routing paths to minimize latency, balance load, and improve throughput. Comparative analysis against existing routing approaches demonstrates significant improvements in packet delivery efficiency, energy consumption, and scalability. The study further explores the implications of dynamic routing in heterogeneous NoC environments, addressing critical challenges such as deadlock avoidance and real-time adaptability. The results highlight the framework's potential as a scalable and efficient solution for next-generation NoC systems, particularly in AI-driven and high-performance computing applications.
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