Hendrik Borras, M.Sc.

Hendrik Borras is a doctoral student at the Computing Systems Group at the Institute of Computer Engineering at Heidelberg University. His work is primarily concerned with novel hardware architectures for machine learning outside of the digital domain.

The first time he came in contact with analog technology, was at the CosmicPi Project in 2017. Where he co-designed an analog readout system for the detection of cosmic muons, which as a topic carried over into his bachelor thesis (pdf). Ever since he has had an interest in low-level hardare, be it digital or analog. And he found his more recent curiosity in Machine Learning in 2020, when working on his master thesis in the Computing Systems Group of Prof. Holger Fröning (pdf). After a six month stint in Dublin at the Xilinx Research Labs in 2021 he then started as a Ph.D. candidate back in Heidelberg. And has been working on novel hardware architectures for Bayesian Neural Networks and Deep Neural Networks ever since.

Cheers, Hendrik

P.S.: This summary is somewhat over dramaticed, but facturally correct.

Research interests

  • Novel hardware architectures for Deep and Bayesian Neural Networks
  • Noisy hardware, such as photonic and electric analog devices
  • High-throughput and low-latency inference of quantized Deep Neural Networks on FPGAs

Curriculum vitae

  • Shortform: pdf
  • Since 2022, PhD candidate in Computer Engineering, Heidelberg University (Germany)
  • 2021-2021. Research intern at Xilinx Research Labs Dublin, now AMD RADICAL (Ireland)
    • Most of my work done in this context is open-source on Github.
  • 2018-2021, Masters in Physics, Heidelberg University (Germany)
    • Thesis: Exploring Structured Sparsity within Data-Flow Architecture on Reconfigurable Hardware, pdf
  • 2014-2018, Bachelor in Physics, Heidelberg University (Germany)
    • Thesis: Determination of the angular distribution of cosmic ray muons and Development of a low-cost silicon detector, pdf

Recent Service (4-year horizon)

Reviewer or Subreviewer

  • International Conference on Supercomputing (ICS) 2024
  • International Conference on Parallel Processing (ICPP) 2023
  • IoT, Edge, and Mobile for Embedded Machine Learning (ITEM) 2023, colocated with ECML-PKDD 2023
  • IoT, Edge, and Mobile for Embedded Machine Learning (ITEM) 2022, colocated with ECML-PKDD 2023

Recent Teaching (4-year horizon)

Summer term 2023
Teaching Assistant; graduate course “Embedded Machine Learning”
Winter term 2020/21
Teaching Assistant; graduate course “GPU computing”

Publications

  1. Alessandro Pappalardo, Yaman Umuroglu, Michaela Blott, Jovan Mitrevski, Benjamin Hawks, Nhan Tran, Vladimir Loncar, Sioni Summers, Hendrik Borras, Jules Muhizi, Matthew Trahms, Shih-Chieh Hsu, Scott Hauck and Javier M. Duarte
    QONNX: Representing Arbitrary-Precision Quantized Neural Networks
    CoRR, abs/2206.07527, 2022
    @article{DBLP:journals/corr/abs-2206-07527,
      author = {Pappalardo, Alessandro and Umuroglu, Yaman and Blott, Michaela and Mitrevski, Jovan and Hawks, Benjamin and Tran, Nhan and Loncar, Vladimir and Summers, Sioni and Borras, Hendrik and Muhizi, Jules and Trahms, Matthew and Hsu, Shih{-}Chieh and Hauck, Scott and Duarte, Javier M.},
      title = {{QONNX:} Representing Arbitrary-Precision Quantized Neural Networks},
      journal = {CoRR},
      volume = {abs/2206.07527},
      year = {2022},
      url = {https://doi.org/10.48550/arXiv.2206.07527}
      doi = {10.48550/ARXIV.2206.07527},
      eprinttype = {arXiv},
      eprint = {2206.07527},
      timestamp = {Tue, 21 Jun 2022 17:35:15 +0200},
    }
    
  2. Hendrik Borras, Giuseppe Di Guglielmo, Javier M. Duarte, Nicolò Ghielmetti, Benjamin Hawks, Scott Hauck, Shih-Chieh Hsu, Ryan Kastner, Jason Liang, Andres Meza, Jules Muhizi, Tai Nguyen, Rushil Roy, Nhan Tran, Yaman Umuroglu, Olivia Weng, Aidan Yokuda and Michaela Blott
    Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark
    CoRR, abs/2206.11791, 2022
    @article{DBLP:journals/corr/abs-2206-11791,
      author = {Borras, Hendrik and Guglielmo, Giuseppe Di and Duarte, Javier M. and Ghielmetti, Nicol{\`{o}} and Hawks, Benjamin and Hauck, Scott and Hsu, Shih{-}Chieh and Kastner, Ryan and Liang, Jason and Meza, Andres and Muhizi, Jules and Nguyen, Tai and Roy, Rushil and Tran, Nhan and Umuroglu, Yaman and Weng, Olivia and Yokuda, Aidan and Blott, Michaela},
      title = {Open-source {FPGA-ML} codesign for the MLPerf Tiny Benchmark},
      journal = {CoRR},
      volume = {abs/2206.11791},
      year = {2022},
      url = {https://doi.org/10.48550/arXiv.2206.11791}
      doi = {10.48550/ARXIV.2206.11791},
      eprinttype = {arXiv},
      eprint = {2206.11791},
      timestamp = {Wed, 29 Jun 2022 11:10:54 +0200},
    }
    
  3. Hendrik Borras, Bernhard Klein and Holger Fröning
    Walking Noise: Understanding Implications of Noisy Computations on Classification Tasks
    CoRR, abs/2212.10430, 2022
    @article{DBLP:journals/corr/abs-2212-10430,
      author = {Borras, Hendrik and Klein, Bernhard and Fr{\"{o}}ning, Holger},
      title = {Walking Noise: Understanding Implications of Noisy Computations on
                        Classification Tasks},
      journal = {CoRR},
      volume = {abs/2212.10430},
      year = {2022},
      url = {https://doi.org/10.48550/arXiv.2212.10430}
      doi = {10.48550/ARXIV.2212.10430},
      eprinttype = {arXiv},
      eprint = {2212.10430},
      timestamp = {Wed, 04 Jan 2023 00:00:00 +0100},
    }
    

Hobbies

  • Home automation
    • In particular enviromental control of my ultra humid flat.
  • Gaming (Valheim, DRG, Ghostrunner, LoL, Nier, Beat Saber, Valorant, …)
  • Tinkering with any digital or physical technology that peaks my interst.
  • Swimming and diving (if I ever find the time…)