LAUSR

The fully connected 3D-NoCs in which all routers are vertically connected with their neighbors above and below need a lot of Through-Silicon-Vias (TSVs), and they will occupy a large silicon area and reduce the fabrication yield. Thus, the idea of partially connected 3D-NoCs has emerged. The optimal number and placement of the vertical links (elevators) must be determined at the chip design stage, which is a multiobjective optimization problem of the performance and the cost. However, optimizing the static elevator placement needs a great amount of calculation and we can not examine all possible solutions at design time. Therefore, we propose a hybrid heuristic strategy for the static elevator placement and assignment, in which the genetic algorithm and the tabu search are combined. The dynamic assignment method is essential for the partially connected 3D-NoCs, and it leads to different traffic distributions and therefore has a huge impact on performance. Many previous static assignment methods can not dynamically change the elevator assignment according to the real-time states of the network, thus it may lead to network congestion. A congestion-aware dynamic assignment (CDA) scheme is proposed in this article, which considers the impact of the distance factor and the congestion factor on the network performance. Experiments show that the proposed CDA method can improve the network performance by 67%–86% compared with the random selection algorithm and can improve the reliability of the partially connected 3D-NoC as well. The key component for the CDA method, the path selection module (PSM), is implemented in FPGA, and the results show that its area cost is negligible compared with a router.