scholarly journals Neuromorphic Computing: Designed Memristor Circuit for Self‐Limited Analog Switching and its Application to a Memristive Neural Network (Adv. Electron. Mater. 6/2019)

2019 ◽  
Vol 5 (6) ◽  
pp. 1970032
Author(s):  
Hanchan Song ◽  
Young Seok Kim ◽  
Juseong Park ◽  
Kyung Min Kim
Keyword(s):  
Neural Network ◽  
Neuromorphic Computing ◽  
Memristive Neural Network

Design of Memristive Neural Network PID Controller

2014 ◽  
Vol 36 (12) ◽  
pp. 2577-2586 ◽  
Author(s):  
Si-Wei XIA ◽  
Shu-Kai DUAN ◽  
Li-Dan WANG ◽  
Xiao-Fang HU
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Memristive Neural Network ◽  
Neural Network Pid

scholarly journals Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Recognition Accuracy ◽  
Material Selection ◽  
Weighted Sum ◽  
Device Design ◽  
Key Factors ◽  
Neuromorphic Computing ◽  
Device Scaling ◽  
The Impact

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

Experimental verification of a memristive neural network

2018 ◽  
Vol 93 (4) ◽  
pp. 1823-1840 ◽  
Author(s):  
I. Carro-Pérez ◽  
C. Sánchez-López ◽  
H. G. González-Hernández
Keyword(s):  
Neural Network ◽  
Experimental Verification ◽  
Memristive Neural Network

Designed Memristor Circuit for Self‐Limited Analog Switching and its Application to a Memristive Neural Network

2019 ◽  
Vol 5 (6) ◽  
pp. 1800740 ◽  
Author(s):  
Hanchan Song ◽  
Young Seok Kim ◽  
Juseong Park ◽  
Kyung Min Kim
Keyword(s):  
Neural Network ◽  
Memristive Neural Network

scholarly journals Adaptive sparse coding based on memristive neural network with applications

2019 ◽  
Vol 13 (5) ◽  
pp. 475-488 ◽  
Author(s):  
Xun Ji ◽  
Xiaofang Hu ◽  
Yue Zhou ◽  
Zhekang Dong ◽  
Shukai Duan
Keyword(s):  
Neural Network ◽  
Sparse Coding ◽  
Memristive Neural Network ◽  
Adaptive Sparse Coding
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