---
_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
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.
IEEE Transactions on Signal Processing. 2020;68:4268-4282. doi:10.1109/TSP.2020.3010355'
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. IEEE Transactions
on Signal Processing. IEEE. https://doi.org/10.1109/TSP.2020.3010355'
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.” IEEE Transactions on Signal Processing. IEEE, 2020. https://doi.org/10.1109/TSP.2020.3010355.'
ieee: 'N. M. Gurel et al., “Compressive sensing using iterative hard thresholding
with low precision data representation: Theory and applications,” IEEE Transactions
on Signal Processing, 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.” IEEE Transactions
on Signal Processing, vol. 68, IEEE, 2020, pp. 4268–82, doi:10.1109/TSP.2020.3010355.'
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'
...