In the era of Internet of Things and Big Data, unconventional techniques are rising
to accommodate the large size of data and the resource constraints. New computing
structures are advancing based on non-volatile memory technologies and different
processing paradigms. Additionally, the intrinsic resiliency of current applications
leads to the development of creative techniques in computations. In those applications,
approximate computing provides a perfect fit to optimize the energy efficiency
while compromising on the accuracy. In this work, we build probabilistic adders
based on stochastic memristor. Probabilistic adders are analyzed with respect of the
stochastic behavior of the underlying memristors. Multiple adder implementations
are investigated and compared. The memristive probabilistic adder provides a different
approach from the typical approximate CMOS adders. Furthermore, it allows for
a high area saving and design exibility between the performance and power saving.
To reach a similar performance level as approximate CMOS adders, the memristive
adder achieves 60% of power saving. An image-compression application is investigated using the memristive probabilistic adders with the performance and the energy trade-off.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/626192 |
| Date | 10 1900 |
| Creators | Alahmadi, Hamzah |
| Contributors | Salama, Khaled N., Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, He, Jr-Hau, Gao, Xin |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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