Reducing Energy Consumption of Data Transfers Using Runtime Data Type Conversion

  • Michael Bromberger
  • Vincent Heuveline
  • Wolfgang Karl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9637)


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.


Energy reduction Approximate computing Data type conversion 



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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Michael Bromberger
    • 1
    • 2
  • Vincent Heuveline
    • 2
  • Wolfgang Karl
    • 1
  1. 1.Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Heidelberg Institute of Theoretical StudiesHeidelbergGermany

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