---
_id: '8268'
abstract:
- lang: eng
  text: 'Modern scientific instruments produce vast amounts of data, which can overwhelm
    the processing ability of computer systems. Lossy compression of data is an intriguing
    solution, but comes with its own drawbacks, such as potential signal loss, and
    the need for careful optimization of the compression ratio. In this work, we focus
    on a setting where this problem is especially acute: compressive sensing frameworks
    for interferometry and medical imaging. We ask the following question: can the
    precision of the data representation be lowered for all inputs, with recovery
    guarantees and practical performance Our first contribution is a theoretical analysis
    of the normalized Iterative Hard Thresholding (IHT) algorithm when all input data,
    meaning both the measurement matrix and the observation vector are quantized aggressively.
    We present a variant of low precision normalized IHT that, under mild conditions,
    can still provide recovery guarantees. The second contribution is the application
    of our quantization framework to radio astronomy and magnetic resonance imaging.
    We show that lowering the precision of the data can significantly accelerate image
    recovery. We evaluate our approach on telescope data and samples of brain images
    using CPU and FPGA implementations achieving up to a 9x speedup with negligible
    loss of recovery quality.'
acknowledgement: The authors would like to thank Dr. Michiel Brentjens at the Netherlands
  Institute for Radio Astronomy (ASTRON) for providing radio interferometer data and
  Dr. Josip Marjanovic and Dr. Franciszek Hennel at the Magnetic Resonance Technology
  of ETH Zurich for providing their insights on the experiments. CZ and the DS3Lab
  gratefully acknowledge the support from the Swiss Data Science Center, Alibaba,
  Google Focused Research Awards, Huawei, MeteoSwiss, Oracle Labs, Swisscom, Zurich
  Insurance, Chinese Scholarship Council, and the Department of Computer Science at
  ETH Zurich.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Nezihe Merve
  full_name: Gurel, Nezihe Merve
  last_name: Gurel
- first_name: Kaan
  full_name: Kara, Kaan
  last_name: Kara
- first_name: Alen
  full_name: Stojanov, Alen
  last_name: Stojanov
- first_name: Tyler
  full_name: Smith, Tyler
  last_name: Smith
- first_name: Thomas
  full_name: Lemmin, Thomas
  last_name: Lemmin
- first_name: Dan-Adrian
  full_name: Alistarh, Dan-Adrian
  id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
  last_name: Alistarh
  orcid: 0000-0003-3650-940X
- first_name: Markus
  full_name: Puschel, Markus
  last_name: Puschel
- first_name: Ce
  full_name: Zhang, Ce
  last_name: Zhang
citation:
  ama: 'Gurel NM, Kara K, Stojanov A, et al. Compressive sensing using iterative hard
    thresholding with low precision data representation: Theory and applications.
    <i>IEEE Transactions on Signal Processing</i>. 2020;68:4268-4282. doi:<a href="https://doi.org/10.1109/TSP.2020.3010355">10.1109/TSP.2020.3010355</a>'
  apa: 'Gurel, N. M., Kara, K., Stojanov, A., Smith, T., Lemmin, T., Alistarh, D.-A.,
    … Zhang, C. (2020). Compressive sensing using iterative hard thresholding with
    low precision data representation: Theory and applications. <i>IEEE Transactions
    on Signal Processing</i>. IEEE. <a href="https://doi.org/10.1109/TSP.2020.3010355">https://doi.org/10.1109/TSP.2020.3010355</a>'
  chicago: 'Gurel, Nezihe Merve, Kaan Kara, Alen Stojanov, Tyler Smith, Thomas Lemmin,
    Dan-Adrian Alistarh, Markus Puschel, and Ce Zhang. “Compressive Sensing Using
    Iterative Hard Thresholding with Low Precision Data Representation: Theory and
    Applications.” <i>IEEE Transactions on Signal Processing</i>. IEEE, 2020. <a href="https://doi.org/10.1109/TSP.2020.3010355">https://doi.org/10.1109/TSP.2020.3010355</a>.'
  ieee: 'N. M. Gurel <i>et al.</i>, “Compressive sensing using iterative hard thresholding
    with low precision data representation: Theory and applications,” <i>IEEE Transactions
    on Signal Processing</i>, vol. 68. IEEE, pp. 4268–4282, 2020.'
  ista: 'Gurel NM, Kara K, Stojanov A, Smith T, Lemmin T, Alistarh D-A, Puschel M,
    Zhang C. 2020. Compressive sensing using iterative hard thresholding with low
    precision data representation: Theory and applications. IEEE Transactions on Signal
    Processing. 68, 4268–4282.'
  mla: 'Gurel, Nezihe Merve, et al. “Compressive Sensing Using Iterative Hard Thresholding
    with Low Precision Data Representation: Theory and Applications.” <i>IEEE Transactions
    on Signal Processing</i>, vol. 68, IEEE, 2020, pp. 4268–82, doi:<a href="https://doi.org/10.1109/TSP.2020.3010355">10.1109/TSP.2020.3010355</a>.'
  short: N.M. Gurel, K. Kara, A. Stojanov, T. Smith, T. Lemmin, D.-A. Alistarh, M.
    Puschel, C. Zhang, IEEE Transactions on Signal Processing 68 (2020) 4268–4282.
date_created: 2020-08-16T22:00:56Z
date_published: 2020-07-20T00:00:00Z
date_updated: 2023-08-22T08:40:08Z
day: '20'
department:
- _id: DaAl
doi: 10.1109/TSP.2020.3010355
external_id:
  arxiv:
  - '1802.04907'
  isi:
  - '000562044500001'
intvolume: '        68'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1802.04907
month: '07'
oa: 1
oa_version: Preprint
page: 4268-4282
publication: IEEE Transactions on Signal Processing
publication_identifier:
  eissn:
  - '19410476'
  issn:
  - 1053587X
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Compressive sensing using iterative hard thresholding with low precision data
  representation: Theory and applications'
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 68
year: '2020'
...