Implementation of Analog Perceptron as an Essential Element of Configurable Neural Networks
Department of Information and Media Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4222; https://doi.org/10.3390/s20154222
Received: 12 June 2020
/
Revised: 25 July 2020
/
Accepted: 28 July 2020
/
Published: 29 July 2020
(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems)
Perceptron is an essential element in neural network (NN)-based machine learning, however, the effectiveness of various implementations by circuits is rarely demonstrated from chip testing. This paper presents the measured silicon results for the analog perceptron circuits fabricated in a 0.6
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Geng, C.; Sun, Q.; Nakatake, S. Implementation of Analog Perceptron as an Essential Element of Configurable Neural Networks. Sensors 2020, 20, 4222. https://doi.org/10.3390/s20154222
AMA Style
Geng C, Sun Q, Nakatake S. Implementation of Analog Perceptron as an Essential Element of Configurable Neural Networks. Sensors. 2020; 20(15):4222. https://doi.org/10.3390/s20154222
Chicago/Turabian StyleGeng, Chao, Qingji Sun, and Shigetoshi Nakatake. 2020. "Implementation of Analog Perceptron as an Essential Element of Configurable Neural Networks" Sensors 20, no. 15: 4222. https://doi.org/10.3390/s20154222
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
