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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) In IFIP Advances in Information and Communication Technology 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
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
IFIP Advances in Information and Communication Technology
editor
Coskun, Ayse; Burg, Andreas; Reis, Ricardo; Guthaus, Matthew; ; ; and
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
2013-06-14 13:47:37
date last changed
2018-02-22 15:03:31
@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},
  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},
  series       = {IFIP Advances in Information and Communication Technology},
  title        = {An Ultra-Low-Power Application-Specific Processor for Compressed Sensing},
  url          = {http://dx.doi.org/10.1007/978-3-642-45073-0_5},
  volume       = {418},
  year         = {2013},
}