1.
Ștefan, Gheorghe; Alexandru, Dinu
Controlling hardware design behavior using Python based machine learning algorithms Proceedings Article
In: 2021 16th International Conference on Engineering of Modern Electric Systems (EMES), pp. 1-4, 2021.
Abstract | Links | BibTeX | Tags: Training;Analytical models;Reinforcement learning;Tools;Hardware;Software;Data models;reinforcement learning;functional verification;hardware;automation
@inproceedings{9484105,
title = {Controlling hardware design behavior using Python based machine learning algorithms},
author = {Gheorghe Ștefan and Dinu Alexandru},
doi = {10.1109/EMES52337.2021.9484105},
year = {2021},
date = {2021-06-01},
booktitle = {2021 16th International Conference on Engineering of Modern Electric Systems (EMES)},
pages = {1-4},
abstract = {Hardware design and software programming. Two powerful worlds, where functionality is described by coding complex functions which will operate either in silicon or in a processor logic. Hardware provides accurate implementation of application needs at highest speeds. Software provides the welcome flexibility when the system needs an update. And the combination of the two approaches, well represented in industry by System-on-chip[1], leads to the powerful devices which are more and more present in our lives. But the well co-operation between hardware and software bring its advantages even from design phase. Software helps a lot hardware development, through powerful tools used at design, verification, synthesis and all other design steps. Software development is accelerated by hardware platforms which make possible intensive testing and scenarios creation for validation of new program releases. This paper exploits the opportunity of using software in hardware development. It demonstrates, both graphically and numerically, that desired verification scenarios can be faster reached by integrating a powerful machine learning technique in design verification: reinforcement learning.},
keywords = {Training;Analytical models;Reinforcement learning;Tools;Hardware;Software;Data models;reinforcement learning;functional verification;hardware;automation},
pubstate = {published},
tppubtype = {inproceedings}
}
Hardware design and software programming. Two powerful worlds, where functionality is described by coding complex functions which will operate either in silicon or in a processor logic. Hardware provides accurate implementation of application needs at highest speeds. Software provides the welcome flexibility when the system needs an update. And the combination of the two approaches, well represented in industry by System-on-chip[1], leads to the powerful devices which are more and more present in our lives. But the well co-operation between hardware and software bring its advantages even from design phase. Software helps a lot hardware development, through powerful tools used at design, verification, synthesis and all other design steps. Software development is accelerated by hardware platforms which make possible intensive testing and scenarios creation for validation of new program releases. This paper exploits the opportunity of using software in hardware development. It demonstrates, both graphically and numerically, that desired verification scenarios can be faster reached by integrating a powerful machine learning technique in design verification: reinforcement learning.