| Форма представления | Статьи в зарубежных журналах и сборниках |
| Год публикации | 2020 |
| Язык | английский |
|
Мосин Сергей Геннадьевич, автор
|
| Библиографическое описание на языке оригинала |
Mosin S., An Accuracy Improvement of the Neuromorphic Functional Models by Using the Parallel ANN Architecture//2020 IEEE East-West Design and Test Symposium, EWDTS 2020 - Proceedings. - 2020. - Vol., Is.. - Art. № 9225034. |
| Аннотация |
2020 IEEE East-West Design and Test Symposium, EWDTS 2020 - Proceedings |
| Ключевые слова |
machine learning, neuromorphic functional
models, analog components and functional blocks, design
automation |
| Название журнала |
2020 IEEE East-West Design and Test Symposium, EWDTS 2020 - Proceedings
|
| URL |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096410660&doi=10.1109%2fEWDTS50664.2020.9225034&partnerID=40&md5=44e10865be3ed36a3201391b03ff3f77 |
| Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=242885 |
Полная запись метаданных  |
| Поле DC |
Значение |
Язык |
| dc.contributor.author |
Мосин Сергей Геннадьевич |
ru_RU |
| dc.date.accessioned |
2020-01-01T00:00:00Z |
ru_RU |
| dc.date.available |
2020-01-01T00:00:00Z |
ru_RU |
| dc.date.issued |
2020 |
ru_RU |
| dc.identifier.citation |
Mosin S., An Accuracy Improvement of the Neuromorphic Functional Models by Using the Parallel ANN Architecture//2020 IEEE East-West Design and Test Symposium, EWDTS 2020 - Proceedings. - 2020. - Vol., Is.. - Art. № 9225034. |
ru_RU |
| dc.identifier.uri |
https://repository.kpfu.ru/?p_id=242885 |
ru_RU |
| dc.description.abstract |
2020 IEEE East-West Design and Test Symposium, EWDTS 2020 - Proceedings |
ru_RU |
| dc.description.abstract |
Enhancement of the up-to-date computing systems
in performance and memory capacity stimulates
development of new mathematical models and methods
for numerical simulation. Machine learning methods are
widely used nowadays in the electronic design automation.
New mathematical entities are focused onto increasing the
design quality and reducing a time cost. A method of constructing
the neuromorphic functional models (NFM) for
analog components and functional blocks is proposed. An
approach to improvement of the NFM accuracy by partitioning
the domain of definition for output characteristics
according to the threshold coefficient and using the parallel
artificial neural network (ANN) architecture is offered.
The automated synthesis route of the NFM is represented.
The results of experimental study for semiconductor diode
and the voltage rectifier circuit are demonstrated. The
accuracy increasing of the synthesized NFM and circuit
simulation results shown high efficiency of the proposed
method. |
ru_RU |
| dc.language.iso |
ru |
ru_RU |
| dc.subject |
machine learning |
ru_RU |
| dc.subject |
neuromorphic functional
models |
ru_RU |
| dc.subject |
analog components and functional blocks |
ru_RU |
| dc.subject |
design
automation |
ru_RU |
| dc.title |
An Accuracy Improvement of the Neuromorphic Functional Models by Using the Parallel ANN Architecture |
ru_RU |
| dc.type |
Статьи в зарубежных журналах и сборниках |
ru_RU |
|
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