Detail publikace

Approximation of Hardware Accelerators driven by Machine-Learning Models

MRÁZEK, V.

Originální název

Approximation of Hardware Accelerators driven by Machine-Learning Models

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

MRÁZEK, V.

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",
  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"
}