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Three-dimensional network-on-chip (NoC) is the primary interconnection method for 3D-stacked multicore processors due to their excellent scalability and interconnect flexibility. With the support of 3D NoC, 3D non-uniform cache architecture (NUCA) is commonly used to organize the last-level cache (LLC) due to its high capacity and fast access latency. However, owing to the layered structure that leads to longer heat dissipation paths and variable inter-layer cooling efficiency, 3D NoC experiences a severe thermal problem that has a big impact on the reliability and performance of the chip. In traditional memory-to-LLC mapping in 3D NUCA, the traffic load in each node is inconsistent with its heat dissipation capability, causing thermal hotspots. To solve the above problem, we propose a temperature-balanced NUCA mapping mechanism named TB-NUCA. First, the Bayesian optimization algorithm is used to calculate the probability distribution of cache blocks in each node in order to equalize the node temperature. Secondly, the structure of TB-NUCA is designed. Finally, comparative experiments were conducted under random, transpose-2, and shuffle traffic patterns. The experimental results reveal that, compared with the classical NUCA mapping mechanism (S-NUCA), TB-NUCA can increase the mean-time-to-failure (MTTF) of routers by up to 28.13% while reducing the maximum temperature, average temperature, and standard deviation of temperature by a maximum of 4.92%, 4.48%, and 20.46%, respectively.

Die-stacking technology is expanding the space diversity of on-chip communications by leveraging through-silicon-via (TSV) integration and wafer bonding. The 3D network-on-chip (NoC), a combination of die-stacking technology and systematic on-chip communication infrastructure, suffers from increased thermal density and unbalanced heat dissipation across multi-stacked layers, significantly affecting chip performance and reliability. Recent studies have focused on runtime thermal management (RTM) techniques for improving the heat distribution balance, but performance degradations, owing to RTM mechanisms and unbalanced inter-layer traffic distributions, remain unresolved. In this study, we present a Q-function-based traffic- and thermal-aware adaptive routing algorithm, utilizing a reinforcement machine learning technique that gradually incorporates updated information into an RTM-based 3D NoC routing path. The proposed algorithm initially collects deadlock-free directions, based on the RTM and topology information. Subsequently, Q-learning-based decision making (through the learning of regional traffic information) is deployed for performance improvement with more balanced inter-layer traffic. The simulation results show that the proposed routing algorithm can improve throughput by 14.0%&ndash, 28.2%, with a 24.9% more balanced inter-layer traffic load and a 30.6% more distributed inter-layer thermal dissipation on average, compared with those obtained in previous studies of a 3D NoC with an 8 &times, 8 &times, 4 mesh topology.