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Energy-Efficient Machine Learning Algorithms

Elsa Dupraz 1, 2 Lav Varshney 3 
1 Lab-STICC_ENSTAB_CACS_COM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : When designing electronic systems, a standard technique to reduce the energy consumption consists of agressively downscaling the voltage supply. However, due to physical limitations, further reducing the power supply of next generations of electronic devices will make computational units unreliable, which may introduce faults in the computation operations realized on these chips [3]. On the other hand, tolerating faults in the computation operations gives us the opportunity to address a tradeoff between algorithm performance and energy consumption. This is the issue we consider in this talk.
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Submitted on : Wednesday, September 2, 2020 - 8:16:22 AM
Last modification on : Friday, September 2, 2022 - 8:22:10 AM

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  • HAL Id : hal-02925052, version 1

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Elsa Dupraz, Lav Varshney. Energy-Efficient Machine Learning Algorithms. The 7th Conference on Information Theory and Complex Systems (TINKOS 2019), Oct 2019, Belgrade, Serbia. ⟨hal-02925052⟩

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