AdEle: An Adaptive Congestion-and-Energy-Aware Elevator Selection for Partially Connected 3D NoCs

2021/02/16 by Ebadollah Taheri, Taheri, Ebadollah, Ryan Kim +3
Computer Science · Engineering · #Advanced Optical Network Technologies #Distributed #Elevator Systems and Control #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Interconnection Networks and Systems #Parallel #Performance (cs.PF) #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2102.08323

openalex publication_date 2021/02/16 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28

Abstract

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.

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