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MRÁZEK, V.
Original Title
Approximation of Hardware Accelerators driven by Machine-Learning Models : (Embedded Tutorial)
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
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 : (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" }