Intelligent NOC hotspot prediction

Elena Kakoulli, Vassos Soteriou, Theocharis Theocharides

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Hotspots are Network on-Chip (NoC) routers or modules which occasionally receive packetized traffic at a higher rate that they can process. This phenomenon reduces the performance of an NoC, especially in the case wormhole flow-control. Such situations may also lead to deadlocks, raising the need of a hotspot prevention mechanism. Such mechanism can potentially enable the system to adjust its behavior and prevent hotspot formation, subsequently sustaining performance and efficiency. This Chapter presents an Artificial Neural Network-based (ANN) hotspot prediction mechanism, potentially triggering a hotspot avoidance mechanism before the hotspot is formed. The ANN monitors buffer utilization and reactively predicts the location of an about to-be-formed hotspot, allowing enough time for the system to react to these potential hotspots. The neural network is trained using synthetic traffic models, and evaluated using both synthetic and real application traces. Results indicate that a relatively small neural network can predict hotspot formation with accuracy ranges between 76 and 92%.

Original languageEnglish (US)
Title of host publicationVLSI 2010 Annual Symposium
Subtitle of host publicationSelected papers
EditorsNikolaos Voros, Amar Mukherjee, Nicolas Sklavos, Konstantinos Masselos, Michael Huebner
Pages3-16
Number of pages14
DOIs
StatePublished - 2011

Publication series

NameLecture Notes in Electrical Engineering
Volume105 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Keywords

  • Artificial Neural Networks
  • Network on-Chip Hotspots
  • VLSI Systems

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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