KFU. Card publication. An Accuracy Improvement of the Neuromorphic Functional Models by Using the Parallel ANN Architecture

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

AN ACCURACY IMPROVEMENT OF THE NEUROMORPHIC FUNCTIONAL MODELS BY USING THE PARALLEL ANN ARCHITECTURE

Form of presentation

Articles in international journals and collections

Year of publication

2020

��

��

Authors, employees of KFU

Mosin Sergey Gennadevich, author

Bibliographic description in the original language

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.

Annotation

2020 IEEE East-West Design and Test Symposium, EWDTS 2020 - Proceedings

Keywords

machine learning, neuromorphic functional models, analog components and functional blocks, design automation

The name of the journal

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

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=242885&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Mosin Sergey Gennadevich	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/eng/?p_id=242885&p_lang=2	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	Articles in international journals and collections	ru_RU