Implementation of Physics Informed Neural Networks on Edge Device

Xuezhi Zhang*, I. Chyn Wey, Maoyang Xiang, T. Hui Teo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Physics-Informed Neural Networks (PINNs) are integrated with fundamental physical principles to solve complex differential equations relevant to scientific computation and engineering disciplines. As edge computing platforms increasingly deploy applications reliant on numerical equations, a growing necessity emerges for specialized computational modules that execute PINNs efficiently and with high performance. In this study, the effectiveness of various approaches is demonstrated through the implementation of a PINN on a Field Programmable Gate Array (FPGA) to address a nonlinear Ordinary Differential Equation (ODE) corresponding to the Reynolds equation. High-Level Synthesis (HLS) is investigated for real-time applications on resource-sensitive devices. Both parallel and pipeline computing techniques are employed in the approach. An alternative method of implementation involves the direct use of Hardware Description Language (HDL) on hardware platforms, optimizing hardware utilization via piece-wise nonlinear approximation. Experimental results indicate that the hardware-implemented PINN achieves an accuracy of 95% in comparison to the actual solution. It is suggested that edge devices can efficiently employ PINNs when paired with appropriate hardware units.

Original languageEnglish
Title of host publicationProceedings - 2023 16th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages441-445
Number of pages5
ISBN (Electronic)9798350393613
DOIs
StatePublished - 2023
Event16th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2023 - Singapore, Singapore
Duration: 18 12 202321 12 2023

Publication series

NameProceedings - 2023 16th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2023

Conference

Conference16th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2023
Country/TerritorySingapore
CitySingapore
Period18/12/2321/12/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Edge devices
  • Field Programmable Gate Array (FPGA)
  • Hardware description language (HDL)
  • High-Level Synthesis (HLS)
  • Physics Informed Neural Networks (PINN)

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