research-article

CNN-inspired analytical global placement for large-scale heterogeneous FPGAs

Authors:

Yao-Wen ChangAuthors Info & Claims

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

Pages 637 - 642

https://doi.org/10.1145/3489517.3530566

Published: 23 August 2022 Publication History

Abstract

The fast-growing capacity and complexity are challenging for FPGA global placement. Besides, while many recent studies have focused on the eDensity-based placement as its great efficiency and quality, they suffer from redundant frequency translation. This paper presents a CNN-inspired analytical placement algorithm to effectively handle the redundant frequency translation problem for large-scale FPGAs. Specifically, we compute the density penalty by a fully-connected propagation and gradient to a discrete differential convolution backward. With the FPGA heterogeneity, vectorization plays a vital role in self-adjusting the density penalty factor and the learning rate. In addition, a pseudo net model is used to further optimize the site constraints by establishing connections between blocks and their nearest available regions. Finally, we formulate a refined objective function and a degree-specific gradient preconditioning to achieve a robust, high-quality solution. Experimental results show that our algorithm achieves an 8% reduction on HPWL and 15% less global placement runtime on average over leading commercial tools.

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Cited By

Gunter AThomas MGhanathe NWilton SAmrouch HHu JGarg SLin Y(2024)Enabling Risk Management of Machine Learning Predictions for FPGA RoutabilityProceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD10.1145/3670474.3685969(1-9)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3670474.3685969
Gunter AThomas MGhanathe NWilton S(2024)Enabling Risk Management of Machine Learning Predictions for FPGA Routability2024 ACM/IEEE 6th Symposium on Machine Learning for CAD (MLCAD)10.1109/MLCAD62225.2024.10740240(1-9)Online publication date: 9-Sep-2024
https://doi.org/10.1109/MLCAD62225.2024.10740240
Tu KTang XYu CJosipović LChu ZTu KTang XYu CJosipović LChu Z(2024)Physical ImplementationFPGA EDA10.1007/978-981-99-7755-0_10(165-206)Online publication date: 1-Feb-2024
https://doi.org/10.1007/978-981-99-7755-0_10
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cover image ACM Conferences

DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference

July 2022

1462 pages

ISBN:9781450391429

DOI:10.1145/3489517

General Chair:
Rob Oshana
NXP

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 23 August 2022

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DAC '22

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DAC '22: 59th ACM/IEEE Design Automation Conference

July 10 - 14, 2022

California, San Francisco

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Gunter AThomas MGhanathe NWilton SAmrouch HHu JGarg SLin Y(2024)Enabling Risk Management of Machine Learning Predictions for FPGA RoutabilityProceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD10.1145/3670474.3685969(1-9)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3670474.3685969
Gunter AThomas MGhanathe NWilton S(2024)Enabling Risk Management of Machine Learning Predictions for FPGA Routability2024 ACM/IEEE 6th Symposium on Machine Learning for CAD (MLCAD)10.1109/MLCAD62225.2024.10740240(1-9)Online publication date: 9-Sep-2024
https://doi.org/10.1109/MLCAD62225.2024.10740240
Tu KTang XYu CJosipović LChu ZTu KTang XYu CJosipović LChu Z(2024)Physical ImplementationFPGA EDA10.1007/978-981-99-7755-0_10(165-206)Online publication date: 1-Feb-2024
https://doi.org/10.1007/978-981-99-7755-0_10
Qian KLiu YZhang ZWang K(2023)Efficient Implementation of Activation Function on FPGA for Accelerating Neural Networks2023 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS46773.2023.10181406(1-5)Online publication date: 21-May-2023
https://doi.org/10.1109/ISCAS46773.2023.10181406
Liu ZChen WQian KLu HLiu YChen SWang K(2023)microGEMM: An Effective CNN-Based Inference Acceleration for Edge ComputingICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10279108(2474-2479)Online publication date: 28-May-2023
https://doi.org/10.1109/ICC45041.2023.10279108
Goswami PBhatia D(2023)Application of Machine Learning in FPGA EDA Tool DevelopmentIEEE Access10.1109/ACCESS.2023.332235811(109564-109580)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3322358

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