Energy-efficient data-driven routing for hybrid interconnection networks: A machine learning approach
Md. Tareq Mahmud, Uma Maheswar Reddy Nagirireddi, Ke Wang · ITU Journal on Future and Evolving Technologies · 2025
Modern high-performance computing systems have undergone a significant transformation with the adoption of chiplet-based multi-die integration. This approach enables scalable improvements in computational and communication performance while reducing energy consumption and manufacturing costs. However, existing chiplet-based interconnection network designs face challenges in meeting the stringent latency and energy efficiency requirements of edge computing systems, particularly for multicast and broadcast communication. Silicon interposers impose high costs in inter-chiplet communication, primarily due to their restricted throughput capacity. Similarly, wired interconnection designs are ill-suited for managing multicast and broadcast traffic, as their design limitations, such as high hop counts, inadequate bandwidth, and resource constraints, contribute to significant power consumption and latency. Chiplet-based hybrid interconnection designs overcome these challenges by integrating both wired and wireless interconnects that can adapt to diverse communication patterns and requirements. This paper introduces a data-driven, machine learning-based dynamic routing framework that intelligently adapts to communication needs and directs traffic to wired, interposer, or hybrid communication links by analyzing workload patterns.