AdEle：一种面向部分连通三维NoCs的拥塞和能量感知自适应电梯选择算法
AdEle: An Adaptive Congestion-and-Energy-Aware Elevator Selection for Partially Connected 3D NoCs
摘要：
通过降低全连接3D片上网络（NoC）中的垂直连接数量，部分连接3D NoC（PC-3DNoC）有助于缓解可靠性和制造问题。本文提出了一种新的，自适应拥塞和能量感知的电梯选择方案称为AdEle，以改善PC-3DNoCs的流量分布。AdEle采用离线多目标模拟退火算法来寻找好的电梯子集，并采用在线电梯选择策略来增强路由过程中的电梯选择。在不同的实际应用流量和配置场景下，与最先进的技术相比，AdEle平均将网络延迟提高了10.9%（最高可达14.6%），而能耗开销不到6.9%。
英文摘要：
By lowering the number of vertical connections in fully connected 3D networks-on-chip (NoCs), partially connected 3D NoCs (PC-3DNoCs) help alleviate reliability and fabrication issues. This paper proposes a novel, adaptive congestion- and energy-aware elevator-selection scheme called AdEle to improve the traffic distribution in PC-3DNoCs. AdEle employs an offline multi-objective simulated-annealing-based algorithm to find good elevator subsets and an online elevator selection policy to enhance elevator selection during routing. Compared to the state-of- the-art techniques under different real-application traffics and configuration scenarios, AdEle improves the network latency by 10.9% on average (up to 14.6%) with less than 6.9% energy consumption overhead.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
