Abstract
Reducing the energy consumption of today’s microprocessors, for which Approximate Computing (AC) is a promising candidate, is an important and challenging task. AC comprises approaches to relax the accuracy of computations in order to achieve a trade-off between energy efficiency and an acceptable remaining quality of the results. A high amount of energy is consumed by memory transfers. Therefore, we present an approach in this paper that saves energy by converting data before transferring it to memory. We introduce a static approach that can reduce the energy up to a factor of 4. We evaluate different methods to get the highest possible accuracy for a given data width. Extending this approach by a dynamic selection of different storage data types improves the accuracy for a 2D Fast Fourier Transformation by two orders of magnitude compared to the static approach using 16-bit data types, while still retaining the reduction in energy consumption. First results show that such a conversion unit can be integrated in low power processors with negligible impact on the power consumption.
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- 1.
Code is based on the implementation of Paul Bourke http://paulbourke.net/miscellaneous/dft/.
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Acknowledgements
The work was mainly performed during a HiPEAC internship at Movidius, Ireland. Special thanks to Fergal Connor and David Moloney. Additionally, this work was also funded by the Klaus Tschira Foundation.
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Bromberger, M., Heuveline, V., Karl, W. (2016). Reducing Energy Consumption of Data Transfers Using Runtime Data Type Conversion. In: Hannig, F., Cardoso, J.M.P., Pionteck, T., Fey, D., Schröder-Preikschat, W., Teich, J. (eds) Architecture of Computing Systems – ARCS 2016. ARCS 2016. Lecture Notes in Computer Science(), vol 9637. Springer, Cham. https://doi.org/10.1007/978-3-319-30695-7_18
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DOI: https://doi.org/10.1007/978-3-319-30695-7_18
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