Abstract
The implementation of analog neural network and online analog learning circuits based on memristive crossbar has been intensively explored in the recent years. The design of various activation functions is important for neuromorphic circuits and systems, especially deep leaning neural networks. There are several implementations of sigmoid and tangent activation function, while the analog hardware implementation of the neural networks with linear activation functions is an open problem. Therefore, this paper introduces a multilayer perceptron design with linear activation function using TSMC 130 μ mCMOS technology. In this paper, the performance of the proposed linear activation function is illustrated. In addition, the temperature variation and noise analysis are shown.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 245-249 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538659281 |
| DOIs | |
| Publication status | Published - Sep 28 2018 |
| Event | 2nd International Conference on Computing and Network Communications, CoCoNet 2018 - Astana, Kazakhstan Duration: Aug 15 2018 → Aug 17 2018 |
Conference
| Conference | 2nd International Conference on Computing and Network Communications, CoCoNet 2018 |
|---|---|
| Country | Kazakhstan |
| City | Astana |
| Period | 8/15/18 → 8/17/18 |
Fingerprint
Keywords
- Crossbar
- Linear Activation Function
- Memristor
- Multilayer perceptron
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture
- Electrical and Electronic Engineering
Cite this
Analysis of Multilayer Perceptron with Rectifier Linear Unit Activation Function. / Mukhametkhan, Meirambek; Krestinskaya, Olga; James Pappachen, Alex.
Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 245-249 8476902.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Analysis of Multilayer Perceptron with Rectifier Linear Unit Activation Function
AU - Mukhametkhan, Meirambek
AU - Krestinskaya, Olga
AU - James Pappachen, Alex
PY - 2018/9/28
Y1 - 2018/9/28
N2 - The implementation of analog neural network and online analog learning circuits based on memristive crossbar has been intensively explored in the recent years. The design of various activation functions is important for neuromorphic circuits and systems, especially deep leaning neural networks. There are several implementations of sigmoid and tangent activation function, while the analog hardware implementation of the neural networks with linear activation functions is an open problem. Therefore, this paper introduces a multilayer perceptron design with linear activation function using TSMC 130 μ mCMOS technology. In this paper, the performance of the proposed linear activation function is illustrated. In addition, the temperature variation and noise analysis are shown.
AB - The implementation of analog neural network and online analog learning circuits based on memristive crossbar has been intensively explored in the recent years. The design of various activation functions is important for neuromorphic circuits and systems, especially deep leaning neural networks. There are several implementations of sigmoid and tangent activation function, while the analog hardware implementation of the neural networks with linear activation functions is an open problem. Therefore, this paper introduces a multilayer perceptron design with linear activation function using TSMC 130 μ mCMOS technology. In this paper, the performance of the proposed linear activation function is illustrated. In addition, the temperature variation and noise analysis are shown.
KW - Crossbar
KW - Linear Activation Function
KW - Memristor
KW - Multilayer perceptron
UR - http://www.scopus.com/inward/record.url?scp=85055997850&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055997850&partnerID=8YFLogxK
U2 - 10.1109/CoCoNet.2018.8476902
DO - 10.1109/CoCoNet.2018.8476902
M3 - Conference contribution
SP - 245
EP - 249
BT - Proceedings of the 2nd International Conference on Computing and Network Communications, CoCoNet 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -