AdEle+: An Adaptive Congestion-and-Energy-Aware Elevator Selection for Partially Connected 3D Networks-on-Chip

Vertical die stacking of 3D Networks-on-Chip (3D NoCs) is enabled using inter-layer Through-Silicon-Via (TSV) links. However, TSV technology suffers from low reliability and high fabrication costs. To mitigate these costs, Partially Connected 3D NoCs (PC-3DNoCs), which use fewer TSV links, have been introduced. Nevertheless, with fewer vertical links (a.k.a. elevators), elevator-less routers will have to send their traffic to nearby elevators for inter-layer traffic, increasing the traffic load and congestion at these elevators and potentially reducing performance. Therefore, it is important that elevator-less routers choose elevators that balance the traffic load among the available elevators. To address this problem, we present an adaptive congestion- and energy-aware elevator-selection algorithm, called AdEle+. AdEle+ employs an offline multi-objective simulated-annealing-based optimization to find good elevator subsets for routers. During high traffic loads, AdEle+ uses an adaptive and online elevator selection algorithm to select an elevator from the elevator subset to dynamically manage traffic congestion on elevators. Moreover, in low congestion circumstances, AdEle+ switches to a distance-based selection to improve energy efficiency. Compared to state-of-the-art selection algorithms under various PC-3DNoC configurations and traffic patterns, AdEle+ reduces the average latency by 9.5% on average and up to 11.2% while reducing the hardware overhead by 10.1%