An Ultra-Low-Power Application-Specific Processor for Compressed Sensing
(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:
https://lup.lub.lu.se/record/3813244
- author
- Constantin, Jeremy ; Dogan, Ahmed ; Andersson, Oskar LU ; Meinerzhagen, Pascal ; Rodrigues, Joachim LU ; Atienza, David and Burg, Andreas
- organization
- publishing date
- 2013
- 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
- 978-3-642-45073-0
- 978-3-642-45072-3
- 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
- 2024-08-18 07:28:59
@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 = {{978-3-642-45073-0}}, 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}}, }