scholarly journals Cascaded Architecture for Memristor Crossbar Array Based Larger-Scale Neuromorphic Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 61679-61688 ◽  
Author(s):  
Sheng-Yang Sun ◽  
Hui Xu ◽  
Jiwei Li ◽  
Qingjiang Li ◽  
Haijun Liu
Keyword(s):  
Neuromorphic Computing ◽  
Crossbar Array ◽  
Memristor Crossbar

Achieving Complementary Resistive Switching and Multi-bit Storage Goals by Modulating Dual-ion Reaction through Supercritical Fluid-assisted Ammoniation

Nanoscale ◽  
2021 ◽  
Author(s):  
Lei Li ◽  
Tianjiao Dai ◽  
Kuan-Chang Chang ◽  
Rui Zhang ◽  
Xinnan Lin ◽  
...  
Keyword(s):  
Supercritical Fluid ◽  
Resistive Switching ◽  
Neuromorphic Computing ◽  
Crossbar Array ◽  
Memristor Crossbar

Complementary resistive switching (CRS) is a core requirement in memristor crossbar array construction for neuromorphic computing in view of its capability to avoid sneak path current. However, previous approaches to...

scholarly journals A Parasitic Resistance-Adapted Programming Scheme for Memristor Crossbar-Based Neuromorphic Computing Systems

Materials ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4097 ◽  
Author(s):  
Son Ngoc Truong
Keyword(s):  
Modeling Method ◽  
Connection Matrix ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
Crossbar Array ◽  
Parasitic Resistance ◽  
Memristor Crossbar ◽  
Resistance Modeling ◽  
The Impact ◽  
Wire Resistance

Memristor crossbar arrays without selector devices, such as complementary-metal oxide semiconductor (CMOS) devices, are a potential for realizing neuromorphic computing systems. However, wire resistance of metal wires is one of the factors that degrade the performance of memristor crossbar circuits. In this work, we propose a wire resistance modeling method and a parasitic resistance-adapted programming scheme to reduce the impact of wire resistance in a memristor crossbar-based neuromorphic computing system. The equivalent wire resistances for the cells are estimated by analyzing the crossbar circuit using the superposition theorem. For the conventional programming scheme, the connection matrix composed of the target memristance values is used for crossbar array programming. In the proposed parasitic resistance-adapted programming scheme, the connection matrix is updated before it is used for crossbar array programming to compensate the equivalent wire resistance. The updated connection matrix is obtained by subtracting the equivalent connection matrix from the original connection matrix. The circuit simulations are performed to test the proposed wire resistance modeling method and the parasitic resistance-adapted programming scheme. The simulation results showed that the discrepancy of the output voltages of the crossbar between the conventional wire resistance modeling method and the proposed wire resistance modeling method is as low as 2.9% when wire resistance varied from 0.5 to 3.0 Ω. The recognition rate of the memristor crossbar with the conventional programming scheme is 99%, 95%, 81%, and 65% when wire resistance is set to be 1.5, 2.0, 2.5, and 3.0 Ω, respectively. By contrast, the memristor crossbar with the proposed parasitic resistance-adapted programming scheme can maintain the recognition as high as 100% when wire resistance is as high as 3.0 Ω.

Wafer‐Scale 2D Hafnium Diselenide Based Memristor Crossbar Array for Energy‐Efficient Neural Network Hardware

2021 ◽  
pp. 2103376 ◽  
Author(s):  
Sifan Li ◽  
Mei‐Er Pam ◽  
Yesheng Li ◽  
Li Chen ◽  
Yu‐Chieh Chien ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Wafer Scale ◽  
Crossbar Array ◽  
Neural Network Hardware ◽  
Memristor Crossbar

A memristor crossbar array of titanium oxide for non-volatile memory and neuromorphic applications

2017 ◽  
Vol 32 (6) ◽  
pp. 065014 ◽  
Author(s):  
Haider Abbas ◽  
Yawar Abbas ◽  
Son Ngoc Truong ◽  
Kyeong-Sik Min ◽  
Mi Ra Park ◽  
...  
Keyword(s):  
Titanium Oxide ◽  
Crossbar Array ◽  
Non Volatile Memory ◽  
Volatile Memory ◽  
Memristor Crossbar

Memristor Crossbar-Based Neuromorphic Computing System: A Case Study

2014 ◽  
Vol 25 (10) ◽  
pp. 1864-1878 ◽  
Author(s):  
Miao Hu ◽  
Hai Li ◽  
Yiran Chen ◽  
Qing Wu ◽  
Garrett S. Rose ◽  
...  
Keyword(s):  
Computing System ◽  
Neuromorphic Computing ◽  
System A ◽  
Memristor Crossbar
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