Binarized Weight Neural-Network Inspired Ultra-Low Power Speech Recognition Processor with Time-Domain Based Digital-Analog Mixed Approximate Computing

2020 ◽  
Author(s):  
Bo Liu ◽  
Hao Cai ◽  
Yu Gong ◽  
Wentao Zhu ◽  
Yan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Low Power ◽  
Time Domain ◽  
Approximate Computing ◽  
Ultra Low Power

An Ultra-Low-Power Dual-Mode Automatic Sleep Staging Processor Using Neural-Network-Based Decision Tree

2019 ◽  
Vol 66 (9) ◽  
pp. 3504-3516 ◽  
Author(s):  
Shang-Yuan Chang ◽  
Bing-Chen Wu ◽  
Yi-Long Liou ◽  
Rui-Xuan Zheng ◽  
Pei-Lin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Low Power ◽  
Sleep Staging ◽  
Dual Mode ◽  
Ultra Low Power

scholarly journals Adiabatic superconducting cells for ultra-low-power artificial neural networks

2016 ◽  
Vol 7 ◽  
pp. 1397-1403 ◽  
Author(s):  
Andrey E Schegolev ◽  
Nikolay V Klenov ◽  
Igor I Soloviev ◽  
Maxim V Tereshonok
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Low Power ◽  
Power Dissipation ◽  
Radial Basis Function Networks ◽  
Activation Functions ◽  
Ultra Low Power ◽  
Network Algorithms ◽  
Low Power Dissipation

We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.

Sign in / Sign up

Export Citation Format

Share Document