Invertible Neural Networks for Design of Broadband Active Mixers

Oluwaseyi Akinwande; Osama Waqar Bhatti; Xingchen Li; Madhavan Swaminathan

doi:10.1145/3551901.3556491

Invertible Neural Networks for Design of Broadband Active Mixers

Oluwaseyi Akinwande, Osama Waqar Bhatti, Xingchen Li, Madhavan Swaminathan

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

In this work, we present the invertible neural network for predicting the posterior distributions of the design space of broadband active mixers with RF from 100 MHz to 10 GHz. This invertible method gives a fast and accurate model when investigating crucial properties of active mixers such as conversion gain and noise figure. Our results show that the response generated by the invertible neural network model has close correlation with the output response from the circuit simulator.

Original language	English (US)
Title of host publication	MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD
Publisher	Association for Computing Machinery, Inc
Pages	145-151
Number of pages	7
ISBN (Electronic)	9781450394864
DOIs	https://doi.org/10.1145/3551901.3556491
State	Published - Sep 12 2022
Event	4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022 - Snowbird, United States Duration: Sep 12 2022 → Sep 13 2022

Publication series

Name	MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD

Conference

Conference	4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022
Country/Territory	United States
City	Snowbird
Period	9/12/22 → 9/13/22

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Graphics and Computer-Aided Design
Hardware and Architecture

Access to Document

10.1145/3551901.3556491

Cite this

Akinwande, O., Bhatti, O. W., Li, X., & Swaminathan, M. (2022). Invertible Neural Networks for Design of Broadband Active Mixers. In MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD (pp. 145-151). (MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD). Association for Computing Machinery, Inc. https://doi.org/10.1145/3551901.3556491

@inproceedings{12cf8e63f7564e8c813a9c133616f16d,

title = "Invertible Neural Networks for Design of Broadband Active Mixers",

abstract = "In this work, we present the invertible neural network for predicting the posterior distributions of the design space of broadband active mixers with RF from 100 MHz to 10 GHz. This invertible method gives a fast and accurate model when investigating crucial properties of active mixers such as conversion gain and noise figure. Our results show that the response generated by the invertible neural network model has close correlation with the output response from the circuit simulator.",

author = "Oluwaseyi Akinwande and Bhatti, {Osama Waqar} and Xingchen Li and Madhavan Swaminathan",

note = "Funding Information: This material is based upon work supported by the National Science Foundation under Grant No. CNS 16-2137259 - Center for Advanced Electronics through Machine Learning (CAEML). Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022 ; Conference date: 12-09-2022 Through 13-09-2022",

year = "2022",

month = sep,

day = "12",

doi = "10.1145/3551901.3556491",

language = "English (US)",

series = "MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD",

publisher = "Association for Computing Machinery, Inc",

pages = "145--151",

booktitle = "MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD",

}

Akinwande, O, Bhatti, OW, Li, X & Swaminathan, M 2022, Invertible Neural Networks for Design of Broadband Active Mixers. in MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD. MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD, Association for Computing Machinery, Inc, pp. 145-151, 4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022, Snowbird, United States, 9/12/22. https://doi.org/10.1145/3551901.3556491

Invertible Neural Networks for Design of Broadband Active Mixers. / Akinwande, Oluwaseyi; Bhatti, Osama Waqar; Li, Xingchen et al.
MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD. Association for Computing Machinery, Inc, 2022. p. 145-151 (MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Invertible Neural Networks for Design of Broadband Active Mixers

AU - Akinwande, Oluwaseyi

AU - Bhatti, Osama Waqar

AU - Li, Xingchen

AU - Swaminathan, Madhavan

N1 - Funding Information: This material is based upon work supported by the National Science Foundation under Grant No. CNS 16-2137259 - Center for Advanced Electronics through Machine Learning (CAEML). Publisher Copyright: © 2022 Owner/Author.

PY - 2022/9/12

Y1 - 2022/9/12

N2 - In this work, we present the invertible neural network for predicting the posterior distributions of the design space of broadband active mixers with RF from 100 MHz to 10 GHz. This invertible method gives a fast and accurate model when investigating crucial properties of active mixers such as conversion gain and noise figure. Our results show that the response generated by the invertible neural network model has close correlation with the output response from the circuit simulator.

AB - In this work, we present the invertible neural network for predicting the posterior distributions of the design space of broadband active mixers with RF from 100 MHz to 10 GHz. This invertible method gives a fast and accurate model when investigating crucial properties of active mixers such as conversion gain and noise figure. Our results show that the response generated by the invertible neural network model has close correlation with the output response from the circuit simulator.

UR - http://www.scopus.com/inward/record.url?scp=85139178816&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85139178816&partnerID=8YFLogxK

U2 - 10.1145/3551901.3556491

DO - 10.1145/3551901.3556491

M3 - Conference contribution

AN - SCOPUS:85139178816

T3 - MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD

SP - 145

EP - 151

BT - MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD

PB - Association for Computing Machinery, Inc

T2 - 4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022

Y2 - 12 September 2022 through 13 September 2022

ER -

Akinwande O, Bhatti OW, Li X, Swaminathan M. Invertible Neural Networks for Design of Broadband Active Mixers. In MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD. Association for Computing Machinery, Inc. 2022. p. 145-151. (MLCAD 2022 - Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD). doi: 10.1145/3551901.3556491

Invertible Neural Networks for Design of Broadband Active Mixers

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this