Adaptive manycore architectures for big data computing

Janardhan Rao Doppa, Ryan Gary Kim, Mihailo Isakov, Michel A. Kinsy, Hyoukjun Kwon, Tushar Krishna

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


This work presents a cross-layer design of an adaptive manycore architecture to address the computational needs of emerging big data applications within the technological constraints of power and reliability. From the circuits end, we present links with reconfigurable repeaters that allow single-cycle traversals across multiple hops, creating fast single-cycle paths on demand. At the microarchitecture end, we present a router with bi-directional links, unified virtual channel (VC) structure, and the ability to perform self-monitoring and self-configuration around faults. We present our vision for self-aware manycore architectures and argue that machine learning techniques are very appropriate to efficiently control various configurable on-chip resources in order to realize this vision. We provide concrete learning algorithms for core and NoC reconfiguration; and dynamic power management to improve the performance, energyefficiency, and reliability over static designs to meet the demands of big data computing. We also discuss future challenges to push the state-of-the-art on fully adaptive manycore architectures.

Original languageEnglish (US)
Title of host publication2017 11th IEEE/ACM International Symposium on Networks-on-Chip, NOCS 2017
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9781450349840
StatePublished - Oct 19 2017
Externally publishedYes
Event11th IEEE/ACM International Symposium on Networks-on-Chip, NOCS 2017 - Seoul, Korea, Republic of
Duration: Oct 19 2017Oct 20 2017

Publication series

Name2017 11th IEEE/ACM International Symposium on Networks-on-Chip, NOCS 2017


Conference11th IEEE/ACM International Symposium on Networks-on-Chip, NOCS 2017
Country/TerritoryKorea, Republic of


  • Adaptive manycore architectures
  • Big data computing
  • Interconnect networks
  • Machine learning
  • Power management

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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