Soft N-modular redundancy: exploiting statistics for robust computation
Kim, Eric P.
Loading…
Permalink
https://hdl.handle.net/2142/14721
Description
Title
Soft N-modular redundancy: exploiting statistics for robust computation
Author(s)
Kim, Eric P.
Issue Date
2010-01-06T16:41:53Z
Director of Research (if dissertation) or Advisor (if thesis)
Shanbhag, Naresh R.
Doctoral Committee Chair(s)
Shanbhag, Naresh R.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
fault tolerance
low power
algorithmic technique
signal processing
N-modular redundancy
Abstract
Achieving energy-efficiency in nanoscale CMOS process technologies is made challenging due to the presence of process, temperature and voltage variations. In this thesis, we present soft N-modular redundancy (soft NMR) that exploits statistics of errors due to these nanoscale artifacts in order to design robust and energy-efficient systems. In contrast to conventional NMR, soft NMR employs estimation and detection techniques in the voter. Analysis of soft NMR, NMR and ANT is given to facilitate future design of systems employing such robust techniques. We also compare NMR and soft NMR in the design of an energy-e cient and robust discrete cosine transform (DCT) image coder. Simulations in a commercial 45 nm, 1:2 V, CMOS process show that soft triple-MR (TMR) provides 10 improvement in robustness and 13% power savings over TMR at the same peak signal-to-noise ratio (PSNR) of 20 dB. In addition, soft dual-MR (DMR) provides a 2x improvement in robustness and a 35% power savings over TMR at the same PSNR of 20 dB. An FPGA implementation of the 2-D DCT showed that hardware emulation results match the RTL simulations.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.