Přístupnostní navigace
E-přihláška
Vyhledávání Vyhledat Zavřít
Detail publikace
MRÁZEK, V.
Originální název
Approximation of Hardware Accelerators driven by Machine-Learning Models : (Embedded Tutorial)
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
The goal of this tutorial is to introduce functional hardware approximation techniques employing machine learning methods. Functional approximation changes the function of a circuit slightly in order to reduce its power consumption. Machine learning models can help to estimate the error and the resulting circuit power consumption. The use of these techniques will be presented at multiple levels - at the individual component level and the higher level of HW accelerator synthesis.
Klíčová slova
approximate computing, machine learning, hardware accelerators
Autoři
Vydáno
3. 5. 2023
Nakladatel
Institute of Electrical and Electronics Engineers
Místo
Tallinn
ISBN
979-8-3503-3277-3
Kniha
Proceedings of International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS '23)
Strany od
91
Strany do
92
Strany počet
2
BibTex
@inproceedings{BUT183763, author="Vojtěch {Mrázek}", title="Approximation of Hardware Accelerators driven by Machine-Learning Models : (Embedded Tutorial)", booktitle="Proceedings of International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS '23)", year="2023", pages="91--92", publisher="Institute of Electrical and Electronics Engineers", address="Tallinn", doi="10.1109/DDECS57882.2023.10139484", isbn="979-8-3503-3277-3" }