scholarly journals Deep Neural Network Optimized to Resistive Memory with Nonlinear Current-Voltage Characteristics

2018 ◽  
Vol 14 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Hyungjun Kim ◽  
Taesu Kim ◽  
Jinseok Kim ◽  
Jae-Joon Kim
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Resistive Memory ◽  
Current Voltage ◽  
Current Voltage Characteristics

Input Voltage Mapping Optimized for Resistive Memory-Based Deep Neural Network Hardware

2017 ◽  
Vol 38 (9) ◽  
pp. 1228-1231 ◽  
Author(s):  
Taesu Kim ◽  
Hyungjun Kim ◽  
Jinseok Kim ◽  
Jae-Joon Kim
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Input Voltage ◽  
Resistive Memory ◽  
Voltage Mapping ◽  
Neural Network Hardware

Array-Level Programming of 3-Bit per Cell Resistive Memory and Its Application for Deep Neural Network Inference

2020 ◽  
Vol 67 (11) ◽  
pp. 4621-4625 ◽  
Author(s):  
Yandong Luo ◽  
Xu Han ◽  
Zhilu Ye ◽  
Hugh Barnaby ◽  
Jae-Sun Seo ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Network Inference ◽  
Resistive Memory

scholarly journals Численное моделирование вольт-амперных характеристик двуслойной резистивной памяти на основе нестехиометрических оксидов металлов

2019 ◽  
Vol 53 (9) ◽  
pp. 1275
Author(s):  
Г.М. Умнягин ◽  
В.Е. Дегтярев ◽  
C.В. Оболенский
Keyword(s):  
Current Amplitude ◽  
Memory Structure ◽  
Resistive Memory ◽  
Conductive Filament ◽  
Truncated Cone ◽  
Current Voltage ◽  
Inclination Angles ◽  
Different Shapes ◽  
Current Voltage Characteristics

AbstractThe current–voltage characteristics of a resistive-memory structure based on non-stoichiometric tantalum oxides is numerically simulated. The results of pulsed studies of structures with different shapes of the conductive filament, such as a truncated cone with different generatrix inclination angles, are presented. It is shown how the shape and total volume of the conductive filament affects the current amplitude and the number of pulses necessary for complete filament breaking and restoration.

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