Παρακαλώ χρησιμοποιήστε αυτό το αναγνωριστικό για να παραπέμψετε ή να δημιουργήσετε σύνδεσμο προς αυτό το τεκμήριο: https://hdl.handle.net/20.500.14279/9013
Τίτλος: A holistic approach towards intelligent hotspot prevention in network-on-chip-based multicores
Συγγραφείς: Soteriou, Vassos 
Theocharides, Theocharis 
Kakoulli, Elena 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Λέξεις-κλειδιά: Multiprocessor interconnection;Neural network hardware;On-chip network;Ultra-scale integration
Ημερομηνία Έκδοσης: 1-Μαρ-2016
Πηγή: IEEE Transactions on Computers, 2016, vol. 65, no. 3, pp. 819-833
Volume: 65
Issue: 3
Start page: 819
End page: 833
Περιοδικό: IEEE Transactions on Computers 
Περίληψη: Traffic hotspots, a severe form of network congestion, can be caused unexpectedly in a network-on-chip (NoC) due to the immanent spatio-temporal unevenness of application traffic. Hotspots reduce the NoC's effective throughput, where in the worst-case scenario, network traffic flows can be frozen indefinitely. To alleviate this problematic phenomenon several adaptive routing algorithms employ online load-balancing schemes, aiming to reduce the possibility of hotspots arising. Since most are not explicitly hotspot-agnostic, they cannot completely prevent hotspot formation(s) as their reactive capability to hotspots is merely passive. This paper presents a pro-active Hotspot-Preventive Routing Algorithm (HPRA) which uses the advance knowledge gained from network-embedded artificial neural network-based (ANN) hotspot predictors to guide packet routing in mitigating any unforeseen near-future hotspot occurrences. First, these ANN-based predictors are trained offline and during multicore operation they gather online statistical data to predict about-to-be-formed hotspots, promptly informing HPRA to take appropriate hotspot-preventive action(s). Next, in a holistic approach, additional ANN training is performed with data acquired after HPRA interferes, so as to further improve hotspot prediction accuracy; hence, the ANN mechanism does not only predict hotspots, but is also aware of changes that HPRA imposes upon the interconnect infrastructure. Evaluation results, including utilizing real application traffic traces gathered from parallelized workload executions onto a chip multiprocessor architecture, show that HPRA can improve network throughput up to 81 percent when compared with prior-art. Hardware synthesis results affirm the HPRA mechanism's moderate overhead requisites.
URI: https://hdl.handle.net/20.500.14279/9013
ISSN: 00189340
DOI: 10.1109/TC.2015.2435748
Rights: © IEEE
Type: Article
Affiliation: Cyprus University of Technology 
University of Cyprus 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Άρθρα/Articles

CORE Recommender
Δείξε την πλήρη περιγραφή του τεκμηρίου

SCOPUSTM   
Citations

14
checked on 9 Νοε 2023

WEB OF SCIENCETM
Citations

9
Last Week
0
Last month
0
checked on 29 Οκτ 2023

Page view(s)

459
Last Week
0
Last month
30
checked on 13 Μαρ 2025

Google ScholarTM

Check

Altmetric


Όλα τα τεκμήρια του δικτυακού τόπου προστατεύονται από πνευματικά δικαιώματα