Approximate computing is a computation which returns a possibly inaccurate result rather than a guaranteed accurate result, for a situation where an approximate result is sufficient for a purpose. One example of such situation is for a search engine where no exact answer may exist for a certain search query and hence, many answers may be acceptable. Similarly, occasional dropping of some frames in a video application can go undetected due to perceptual limitations of humans. Approximate computing is based on the observation that in many scenarios, although performing exact computation requires large amount of resources, allowing bounded approximation can provide disproportionate gains in performance and energy, while still achieving acceptable result accuracy.
A. Hanif, R. Hafiz, O. Hasan and M. Shafique, “QuAd: Design and Analysis of Quality-Area Optimal Low-Latency Approximate Adders”, Design Automation Conference (DAC-2017), Austin, TX, USA, To Appear.
M. K. Ayub, O. Hasan and M. Shafique, “Statistical Error Analysis for Low Power Adders”, Design Automation Conference (DAC-2017), Austin, TX, USA, To Appear.
S. Mazahir, O. Hasan, R. Hafiz, M. Shafique, and J. Henkel. “Probabilistic Error Modeling for Approximate Adders,” IEEE Transactions on Computers, To Appear, 2016
S. Mazahir, O. Hasan, R. Hafiz, M. Shafique and J. Henkel,”An Area-Efficient Consolidated Configurable Error Correction for Approximate Hardware Accelerators“, Design Automation Conference (DAC-2016), Austin, TX, USA, 96:1-96:6. (Rank A, CORE)