University of Illinois Urbana-Champaign

Acceleration of deep learning applications for astrophysics on FPGAs

Yun, Mengshen

Content Files
YUN-THESIS-2021.pdf

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https://hdl.handle.net/2142/114026

Description

Title
Acceleration of deep learning applications for astrophysics on FPGAs
Author(s)
Yun, Mengshen
Issue Date
2021-12-09
Director of Research (if dissertation) or Advisor (if thesis)
Kindratenko, Volodymyr
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2022-04-29T21:47:47Z
Keyword(s)
  • FPGAs
  • neural networks
  • deep learning
  • astrophysics
Abstract
Deep learning algorithms have been widely used in the past decade due to their effectiveness and robustness in information processing in various scientific domains. With the evolvement of large-scale datasets and deep learning applications used for gravitational wave astrophysics and large-scale electromagnetic surveys, an accessible and efficient framework is essential to accelerate domain-specific applications on acceleration platforms. Although CPUs and GPUs are common platforms for training and inferencing deep learning applications given their ease-of-use and support for popular deep learning frameworks, field-programmable gate array (FPGA)-based accelerators have rapidly gained their popularity because of their capacity to deliver superior performance for real-time applications with relatively low power consumption. In this thesis, we investigate the process of porting deep learning algorithms developed for Multi-Messenger Astrophysics and particle physics on FPGA accelerators and propose a cyberinfrastructure that enables the use of FPGAs to accelerate algorithms inference. Also, we explore and evaluate three main development stacks, hls4ml, Vitis AI, and TVM, used for deploying deep neural network (DNN) models on cloud FPGA-based hardware accelerators.
Graduation Semester
2021-12
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/114026
Copyright and License Information
Copyright 2021 Mengshen Yun

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