Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

An Ultra-Low-Power Application-Specific Processor for Compressed Sensing

Constantin, Jeremy ; Dogan, Ahmed ; Andersson, Oskar LU ; Meinerzhagen, Pascal ; Rodrigues, Joachim LU ; Atienza, David and Burg, Andreas (2013) 418. p.88-106
Abstract
Compressed sensing (CS) is a universal low-complexity data compression technique for signals that have a sparse representation in some domain. While CS data compression can be done both in the analog- and digital domain, digital implementations are often used on low-power sensor nodes, where an ultra-low-power (ULP) processor carries out the algorithm on Nyquist-rate sampled data. In such systems an energy-efficient implementation of the CS compression kernel is a vital ingredient to maximize battery lifetime. In this paper, we propose an application-specific instruction-set processor (ASIP) processor that has been optimized for CS data compression and for operation in the subthreshold (sub-VT) regime. The design is equipped with specific... (More)
Compressed sensing (CS) is a universal low-complexity data compression technique for signals that have a sparse representation in some domain. While CS data compression can be done both in the analog- and digital domain, digital implementations are often used on low-power sensor nodes, where an ultra-low-power (ULP) processor carries out the algorithm on Nyquist-rate sampled data. In such systems an energy-efficient implementation of the CS compression kernel is a vital ingredient to maximize battery lifetime. In this paper, we propose an application-specific instruction-set processor (ASIP) processor that has been optimized for CS data compression and for operation in the subthreshold (sub-VT) regime. The design is equipped with specific sub-VT capable standard-cell based memories, to enable low-voltage operation with low leakage. Our results show that the proposed ASIP accomplishes 62× speed-up and 11.6× power savings with respect to a straightforward CS implementation running on the baseline low-power processor without instruction set extensions. (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
IFIP Advances in Information and Communication Technology
editor
Coskun, Ayse ; Burg, Andreas ; Reis, Ricardo and Guthaus, Matthew
volume
418
pages
88 - 106
publisher
Springer
external identifiers
  • scopus:84944572195
ISBN
978-3-642-45072-3
978-3-642-45073-0
DOI
10.1007/978-3-642-45073-0_5
language
English
LU publication?
yes
id
35a9d492-c473-4f90-8884-cc4e38458a8f (old id 3813244)
date added to LUP
2016-04-04 12:17:23
date last changed
2024-01-13 04:21:07
@inbook{35a9d492-c473-4f90-8884-cc4e38458a8f,
  abstract     = {{Compressed sensing (CS) is a universal low-complexity data compression technique for signals that have a sparse representation in some domain. While CS data compression can be done both in the analog- and digital domain, digital implementations are often used on low-power sensor nodes, where an ultra-low-power (ULP) processor carries out the algorithm on Nyquist-rate sampled data. In such systems an energy-efficient implementation of the CS compression kernel is a vital ingredient to maximize battery lifetime. In this paper, we propose an application-specific instruction-set processor (ASIP) processor that has been optimized for CS data compression and for operation in the subthreshold (sub-VT) regime. The design is equipped with specific sub-VT capable standard-cell based memories, to enable low-voltage operation with low leakage. Our results show that the proposed ASIP accomplishes 62× speed-up and 11.6× power savings with respect to a straightforward CS implementation running on the baseline low-power processor without instruction set extensions.}},
  author       = {{Constantin, Jeremy and Dogan, Ahmed and Andersson, Oskar and Meinerzhagen, Pascal and Rodrigues, Joachim and Atienza, David and Burg, Andreas}},
  booktitle    = {{IFIP Advances in Information and Communication Technology}},
  editor       = {{Coskun, Ayse and Burg, Andreas and Reis, Ricardo and Guthaus, Matthew}},
  isbn         = {{978-3-642-45072-3}},
  language     = {{eng}},
  pages        = {{88--106}},
  publisher    = {{Springer}},
  title        = {{An Ultra-Low-Power Application-Specific Processor for Compressed Sensing}},
  url          = {{http://dx.doi.org/10.1007/978-3-642-45073-0_5}},
  doi          = {{10.1007/978-3-642-45073-0_5}},
  volume       = {{418}},
  year         = {{2013}},
}