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m-SAAC: Multi-stage adaptive approximation control to select approximate computing modes for vision applications
The psycho-visual nature of images and iterative nature of processing algorithms make vision and image processing suitable applications for approximate computing. State-of-the-art research in this area examines application resilience to approximation while assuming a uniform distribution for the inf...
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Published in: | Microelectronics 2019-09, Vol.91, p.84-91 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The psycho-visual nature of images and iterative nature of processing algorithms make vision and image processing suitable applications for approximate computing. State-of-the-art research in this area examines application resilience to approximation while assuming a uniform distribution for the information source. In this paper, we demonstrate that data-driven analysis can provide better insight into approximation requirements for image processing applications. Furthermore, this analysis is leveraged to design the multi-stage adaptive approximation control (m-SAAC) methodology that can save compute power by utilizing approximate computing, without compromising on image quality. The results demonstrate the efficacy of the proposed methodology for a variety of test cases. |
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ISSN: | 1879-2391 1879-2391 |
DOI: | 10.1016/j.mejo.2019.07.010 |