arrow
Return

Robust and Energy Efficient Multimedia Systems via Likelihood Processing

delete2013-02-01
delete6
PRE
AI
R
Rami A. Abdallah *
N
Naresh R. Shanbhag
DOI:10.1109/TMM.2012.2231667delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
This paper presents likelihood processing (LP) for designing robust and energy-efficient multimedia systems in the presence of nanoscale non-idealities. LP exploits error statistics of the underlying hardware to compute the probability of a particular bit being a one or a zero. Multiple output observations are generated via either: 1) modular redundancy (MR), 2) estimation, or 3) exploiting spatio-temporal correlation. Energy efficiency and robustness of a 2D discrete-cosine transform (DCT) image codec employing LP is studied. Simulations in a commercial 45-nm CMOS process show that LP can tolerate up to 100x, and 5x greater component error probability as compared to conventional and triple-MR (TMR)-based systems, respectively, while achieving a peak-signal-to-noise ratio (PSNR) of 30 dB at a pre-correction error rate of 20%. Furthermore, LP is able to achieve energy savings of 71% over TMR at a PSNR of 28 dB, while tolerating a pre-correction error rate of 4%.
Keywords:
Error resiliency
low-power design
media processing
robust design
stochastic computation
voltage overscaling
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Transactions on Multimedia cover
IEEE Transactions on Multimedia
IF:
9.7
Papers:
4.7K
Citations:
2.4W

Organization

University of Illinois System cover
University of Illinois System
Scholars:
6.9W
Papers: 6.2W
Citations: 644
Cited Papers

Cited Papers

Soft N-Modular Redundancy
err2012-03-01
err56
PREAI
errKim, Eric P.; Shanbhag, Naresh R.
errShare
errSave
Immune defence in bumble-bee offspring
err2001-11-01
err0
errOAAI
errYannick Moret; Paul Schmid-Hempel
errShare
errSave
researcher View more