Result Details

Approximation of Hardware Accelerators driven by Machine-Learning Models

MRÁZEK, V. Approximation of Hardware Accelerators driven by Machine-Learning Models. In Proceedings of International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS '23). Tallinn: Institute of Electrical and Electronics Engineers, 2023. p. 91-92. ISBN: 979-8-3503-3277-3.
Type
conference paper
Language
English
Authors
Mrázek Vojtěch, Ing., Ph.D., DCSY (FIT)
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

Published
2023
Pages
91–92
Proceedings
Proceedings of International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS '23)
Conference
International Symposium on Design and Diagnostics of Electronic Circuits and Systems
ISBN
979-8-3503-3277-3
Publisher
Institute of Electrical and Electronics Engineers
Place
Tallinn
DOI
10.1109/DDECS57882.2023.10139484
UT WoS
001012062000018
EID Scopus
2-s2.0-85162246870
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"
}
Projects
Automated design of hardware accelerators for resource-aware machine learning, GACR, Standardní projekty, GA21-13001S, start: 2021-01-01, end: 2023-12-31, completed
Research groups
Evolvable Hardware Research Group (RG EHW)
Departments
Department of Computer Systems (DCSY)
Back to top