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COMBA: a comprehensive model-based analysis framework for high level synthesis of real applications

Authors:

Bingsheng HeAuthors Info & Claims

ICCAD '17: Proceedings of the 36th International Conference on Computer-Aided Design

Pages 430 - 437

Published: 13 November 2017 Publication History

Abstract

High Level Synthesis (HLS) relies on the use of synthesis pragmas to generate digital designs meeting a set of specifications. However, the selection of a set of pragmas depends largely on designer experience and knowledge of the target architecture and digital design. Existing automated methods of pragma selection are very limited in scope and capability to analyze complex design descriptions in high-level languages to be synthesized using HLS. In this paper, we propose COMBA, a comprehensive model-based analysis framework capable of analyzing the effects of a multitude of pragmas related to functions, loops and arrays in the design description using pluggable analytical models, a recursive data collector (RDC) and a metric-guided design space exploration algorithm (MGDSE). When compared with HLS tools like Vivado HLS, COMBA reports an average error of around 1% in estimating performance, while taking only a few seconds for analysis of Polybench benchmark applications and a few minutes for real-life applications like JPEG, Seidel and Rician. The synthesis pragmas recommended by COMBA result in an average 100x speed-up in performance for the analyzed applications, which establishes COMBA as a superior alternative to current state-of-the-art approaches.

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Cited By

Alcorta Lozano EGerstlauer ADeng CSun QZhang ZXu CWills LSanchez Lopera DEcker WGarg SHu JAl Faruque MHu X(2023)Special Session: Machine Learning for Embedded System DesignProceedings of the 2023 International Conference on Hardware/Software Codesign and System Synthesis10.1145/3607888.3608962(28-37)Online publication date: 17-Sep-2023
https://dl.acm.org/doi/10.1145/3607888.3608962
Wang ZSchafer B(2022)Learning from the Past: Efficient High-level Synthesis Design Space Exploration for FPGAsACM Transactions on Design Automation of Electronic Systems10.1145/349553127:4(1-23)Online publication date: 12-Feb-2022
https://dl.acm.org/doi/10.1145/3495531
Sohrabizadeh AYu CGao MCong J(2022)AutoDSE: Enabling Software Programmers to Design Efficient FPGA AcceleratorsACM Transactions on Design Automation of Electronic Systems10.1145/349453427:4(1-27)Online publication date: 12-Feb-2022
https://dl.acm.org/doi/10.1145/3494534
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Information & Contributors

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Published In

cover image ACM Conferences

ICCAD '17: Proceedings of the 36th International Conference on Computer-Aided Design

November 2017

1077 pages

Conference Chair:
Sri Parameswaran
GENERAL CHAIR

Sponsors

CEDA: Council on Electronic Design Automation
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CAS: Circuits & Systems

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IEEE-EDS: Electronic Devices Society

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IEEE Press

Publication History

Published: 13 November 2017

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ICCAD '17

Sponsor:

CEDA
SIGDA
IEEE-CAS

ICCAD '17: IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN

November 13 - 16, 2017

California, Irvine

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Overall Acceptance Rate 457 of 1,762 submissions, 26%

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Cited By

Alcorta Lozano EGerstlauer ADeng CSun QZhang ZXu CWills LSanchez Lopera DEcker WGarg SHu JAl Faruque MHu X(2023)Special Session: Machine Learning for Embedded System DesignProceedings of the 2023 International Conference on Hardware/Software Codesign and System Synthesis10.1145/3607888.3608962(28-37)Online publication date: 17-Sep-2023
https://dl.acm.org/doi/10.1145/3607888.3608962
Wang ZSchafer B(2022)Learning from the Past: Efficient High-level Synthesis Design Space Exploration for FPGAsACM Transactions on Design Automation of Electronic Systems10.1145/349553127:4(1-23)Online publication date: 12-Feb-2022
https://dl.acm.org/doi/10.1145/3495531
Sohrabizadeh AYu CGao MCong J(2022)AutoDSE: Enabling Software Programmers to Design Efficient FPGA AcceleratorsACM Transactions on Design Automation of Electronic Systems10.1145/349453427:4(1-27)Online publication date: 12-Feb-2022
https://dl.acm.org/doi/10.1145/3494534
Lai YUstun EXiang SFang ZRong HZhang Z(2021)Programming and Synthesis for Software-defined FPGA Acceleration: Status and Future ProspectsACM Transactions on Reconfigurable Technology and Systems10.1145/346966014:4(1-39)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3469660
Kwon JCarloni LSchlichtmann UGal RAmrouch HLi H(2020)Transfer Learning for Design-Space Exploration with High-Level SynthesisProceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD10.1145/3380446.3430636(163-168)Online publication date: 16-Nov-2020
https://dl.acm.org/doi/10.1145/3380446.3430636
Syrowik BFort BBrown S(2018)Use of CPU Performance Counters for Accelerator Selection in HLS-Generated CPU-Accelerator SystemsProceedings of the 9th International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies10.1145/3241793.3241805(1-6)Online publication date: 20-Jun-2018
https://dl.acm.org/doi/10.1145/3241793.3241805
Cong JWei PYu CZhang P(2018)Automated accelerator generation and optimization with composable, parallel and pipeline architectureProceedings of the 55th Annual Design Automation Conference10.1145/3195970.3195999(1-6)Online publication date: 24-Jun-2018
https://dl.acm.org/doi/10.1145/3195970.3195999

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