EOmesh: combined flow balancing and deterministic routing for reduced WCET estimates in embedded real-time systems

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The increasing performance needs in critical real-time embedded systems (CRTES) can only be satisfied with the use of high-performance manycore processors. While NoC-based manycore systems are popular in the high-performance domain due to their high average performance, they challenge deriving tight Worst-Case Execution Time (WCET) estimates, as needed in CRTES. Weighted meshes have been proposed to alleviate NoCs pathological behavior – caused by large bandwidth imbalance – by making locally unbalanced arbitration decisions to reach globally balanced bandwidth. In this paper we show that existing weighted mesh solutions do not completely remove unwanted imbalance, in particular for nodes subject to high congestion. We propose EOmesh, an approach that combines heterogeneous predictable routing and weight allocations that delivers near-optimal bandwidth allocation across cores without increasing NoC complexity. EOmesh, which can be implemented either by hardware means or by software means on top of regular weighted meshes, improves the average performance and WCET results of the reference weighted mesh design.This work has been partially supported by the SpanishMinistry of Economy and Competitiveness (MINECO) un-der grant TIN2015-65316-P and the HiPEAC Network ofExcellence. Jaume Abella has been partially supported bythe MINECO under Ramon y Cajal postdoctoral fellowshipnumber RYC-2013-14717. Carles Hern ́andez is jointly fundedby the MINECO and FEDER funds through grant TIN2014-60404-JIN. Francisco J. Cazorla is partially funded by theEuropean Research Council (ERC) under EU’s H2020 research/innovation programme (grant No. 772773)Peer ReviewedPostprint (author's final draft