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MOOS: A Multi-Objective Design Space Exploration and Optimization Framework for NoC Enabled Manycore Systems

Authors:

Nitthilan Kanappan Jayakodi,

Biresh Kumar Joardar,

Janardhan Rao Doppa,

Partha Pratim PandeAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 18, Issue 5s

Article No.: 77, Pages 1 - 23

https://doi.org/10.1145/3358206

Published: 08 October 2019 Publication History

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Abstract

The growing needs of emerging applications has posed significant challenges for the design of optimized manycore systems. Network-on-Chip (NoC) enables the integration of a large number of processing elements (PEs) in a single die. To design optimized manycore systems, we need to establish suitable trade-offs among multiple objectives including power, performance, and thermal. Therefore, we consider multi-objective design space exploration (MO-DSE) problems arising in the design of NoC-enabled manycore systems: placement of PEs and communication links to optimize two or more objectives (e.g., latency, energy, and throughput). Existing algorithms to solve MO-DSE problems suffer from scalability and accuracy challenges as size of the design space and the number of objectives grow. In this paper, we propose a novel framework referred as Multi-Objective Optimistic Search (MOOS) that performs adaptive design space exploration using a data-driven model to improve the speed and accuracy of multi-objective design optimization process. We apply MOOS to design both 3D heterogeneous and homogeneous manycore systems using Rodinia, PARSEC, and SPLASH2 benchmark suites. We demonstrate that MOOS improves the speed of finding solutions compared to state-of-the-art methods by up to 13X while uncovering designs that are up to 20% better in terms of NoC. The optimized 3D manycore systems improve the EDP up to 38% when compared to 3D mesh-based designs optimized for the placement of PEs.

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Cited By

Ahmadianshalchi ABelakaria SDoppa J(2024)Preference-Aware Constrained Multi-Objective Bayesian OptimizationProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632427(182-191)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632427
Qi SLi YPasricha SKim R(2023)MOELA: A Multi-Objective Evolutionary/Learning Design Space Exploration Framework for 3D Heterogeneous Manycore Platforms2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10137276(1-6)Online publication date: Apr-2023
https://doi.org/10.23919/DATE56975.2023.10137276
Dave SNowatzki TShrivastava AAamodt TSwift MJerger N(2023)Explainable-DSE: An Agile and Explainable Exploration of Efficient HW/SW Codesigns of Deep Learning Accelerators Using Bottleneck AnalysisProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 410.1145/3623278.3624772(87-107)Online publication date: 25-Mar-2023
https://dl.acm.org/doi/10.1145/3623278.3624772
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Index Terms

MOOS: A Multi-Objective Design Space Exploration and Optimization Framework for NoC Enabled Manycore Systems
1. Computing methodologies
  1. Machine learning
2. Hardware
  1. Very large scale integration design
    1. Design reuse and communication-based design
      1. Network on chip

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 18, Issue 5s

Special Issue ESWEEK 2019, CASES 2019, CODES+ISSS 2019 and EMSOFT 2019

October 2019

1423 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/3365919

Editor:
Sandeep K. Shukla
Indian Institute of Technology, India

Issue’s Table of Contents

Copyright © 2019 ACM.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 08 October 2019

Accepted: 01 July 2019

Revised: 01 June 2019

Received: 01 May 2019

Published in TECS Volume 18, Issue 5s

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Cited By

Ahmadianshalchi ABelakaria SDoppa J(2024)Preference-Aware Constrained Multi-Objective Bayesian OptimizationProceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632427(182-191)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632427
Qi SLi YPasricha SKim R(2023)MOELA: A Multi-Objective Evolutionary/Learning Design Space Exploration Framework for 3D Heterogeneous Manycore Platforms2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE56975.2023.10137276(1-6)Online publication date: Apr-2023
https://doi.org/10.23919/DATE56975.2023.10137276
Dave SNowatzki TShrivastava AAamodt TSwift MJerger N(2023)Explainable-DSE: An Agile and Explainable Exploration of Efficient HW/SW Codesigns of Deep Learning Accelerators Using Bottleneck AnalysisProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 410.1145/3623278.3624772(87-107)Online publication date: 25-Mar-2023
https://dl.acm.org/doi/10.1145/3623278.3624772
Boroujerdian BJing YTripathy DKumar ASubramanian LYen LLee VVenkatesan VJindal AShearer RReddi V(2023)FARSI: An Early-stage Design Space Exploration Framework to Tame the Domain-specific System-on-chip ComplexityACM Transactions on Embedded Computing Systems10.1145/354401622:2(1-35)Online publication date: 24-Jan-2023
https://dl.acm.org/doi/10.1145/3544016
Sambangi RPandey AManna KMahapatra SChattopadhyay S(2023)Application Mapping Onto Manycore Processor Architectures Using Active Search FrameworkIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2023.323985031:6(789-801)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TVLSI.2023.3239850
Joardar BDoppa JPande P(2023)Machine Learning for Heterogeneous Manycore DesignEmbedded Machine Learning for Cyber-Physical, IoT, and Edge Computing10.1007/978-3-031-39932-9_7(175-200)Online publication date: 10-Oct-2023
https://doi.org/10.1007/978-3-031-39932-9_7
Islam R(2022)Feasibility Prediction for Rapid IC Design Space ExplorationElectronics10.3390/electronics1107116111:7(1161)Online publication date: 6-Apr-2022
https://doi.org/10.3390/electronics11071161
Belakaria SDeshwal ADoppa J(2022)Output Space Entropy Search Framework for Multi-Objective Bayesian OptimizationJournal of Artificial Intelligence Research10.1613/jair.1.1296672(667-715)Online publication date: 4-Jan-2022
https://dl.acm.org/doi/10.1613/jair.1.12966
Siddhu LKedia RPandey SRapp MPathania AHenkel JPanda P(2022)CoMeT: An Integrated Interval Thermal Simulation Toolchain for 2D, 2.5D, and 3D Processor-Memory SystemsACM Transactions on Architecture and Code Optimization10.1145/353218519:3(1-25)Online publication date: 22-Aug-2022
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Rapp MAmrouch HLin YYu BPan DWolf MHenkel J(2022)MLCAD: A Survey of Research in Machine Learning for CAD Keynote PaperIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.312476241:10(3162-3181)Online publication date: 1-Oct-2022
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