Motion estimation performance models with application to hardware error tolerance

Hye-Yeon Cheong; Antonio Ortega

doi:10.1117/12.705926

29 January 2007 Motion estimation performance models with application to hardware error tolerance

Hye-Yeon Cheong, Antonio Ortega

Proceedings Volume 6508, Visual Communications and Image Processing 2007; 65081N (2007) https://doi.org/10.1117/12.705926
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

The progress of VLSI technology towards deep sub-micron feature sizes, e.g., sub-100 nanometer technology, has created a growing impact of hardware defects and fabrication process variability, which lead to reductions in yield rate. To address these problems, a new approach, system-level error tolerance (ET), has been recently introduced. Considering that a significant percentage of the entire chip production is discarded due to minor imperfections, this approach is based on accepting imperfect chips that introduce imperceptible/acceptable system-level degradation; this leads to increases in overall effective yield. In this paper, we investigate the impact of hardware faults on the video compression performance, with a focus on the motion estimation (ME) process. More specifically, we provide an analytical formulation of the impact of single and multiple stuck-at-faults within ME computation. We further present a model for estimating the system-level performance degradation due to such faults, which can be used for the error tolerance based decision strategy of accepting a given faulty chip. We also show how different faults and ME search algorithms compare in terms of error tolerance and define the characteristics of search algorithm that lead to increased error tolerance. Finally, we show that different hardware architectures performing the same metric computation have different error tolerance characteristics and we present the optimal ME hardware architecture in terms of error tolerance. While we focus on ME hardware, our work could also applied to systems (e.g., classifiers, matching pursuits, vector quantization) where a selection is made among several alternatives (e.g., class label, basis function, quantization codeword) based on which choice minimizes an additive metric of interest.

Citation Download Citation

Hye-Yeon Cheong and Antonio Ortega "Motion estimation performance models with application to hardware error tolerance", Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65081N (29 January 2007); https://doi.org/10.1117/12.705926

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