Přístupnostní navigace
E-application
Search Search Close
Product detail
MRÁZEK, V.
Product type
software
Abstract
The automated generation of approximate circuits and accelerators has been a useful design strategy to achieve energy efficiency and/or performance improvements. In this work, we propose a framework, autoAx, that leverages machine learning models that evaluate the state-of-the-art approximate components to explore the architecture space effectively. These accelerators are modeled at RTL and optimized using an evolutionary algorithm. The AutoAx framework is extensible, open-source, and can assist in exploring new directions in high-level approximation.
Keywords
approximate computing, high level synthesis, machine learning
Create date
31. 7. 2023
Location
https://github.com/ehw-fit/autoax
Possibilities of use
Využití výsledku jiným subjektem je možné bez nabytí licence (výsledek není licencován)
Licence fee
Poskytovatel licence na výsledek nepožaduje licenční poplatek
www