Publication detail

Approximation of Hardware Accelerators driven by Machine-Learning Models

MRÁZEK, V.

Original Title

Approximation of Hardware Accelerators driven by Machine-Learning Models

Type

conference paper

Language

English

Original Abstract

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.

Keywords

approximate computing, machine learning, hardware accelerators

Authors

MRÁZEK, V.

Released

3. 5. 2023

Publisher

Institute of Electrical and Electronics Engineers

Location

Tallinn

ISBN

979-8-3503-3277-3

Book

Proceedings of International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS '23)

Pages from

91

Pages to

92

Pages count

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"
}