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Fuel ◽  
2022 ◽  
Vol 314 ◽  
pp. 123061
Author(s):  
Shiwei Ge ◽  
Xiaoqing Liu ◽  
Jun Liu ◽  
Hao Liu ◽  
Haiyan Liu ◽  
...  

2022 ◽  
Vol 156 ◽  
pp. 111970
Author(s):  
Maria Mechili ◽  
Christos Vaitsis ◽  
Nikolaos Argirusis ◽  
Pavlos K. Pandis ◽  
Georgia Sourkouni ◽  
...  

2022 ◽  
Vol 236 ◽  
pp. 111511
Author(s):  
Yuxiang Wang ◽  
Yue Liu ◽  
Junye Tong ◽  
Xinan Shi ◽  
Lijian Huang ◽  
...  

2022 ◽  
Vol 284 ◽  
pp. 116991
Author(s):  
Asfand Yar ◽  
Syam G. Krishnan ◽  
John Ojur Dennis ◽  
Amina Yasin ◽  
Mohammad Khalid ◽  
...  

2022 ◽  
Vol 18 (2) ◽  
pp. 1-22
Author(s):  
Alexander Jones ◽  
Aaron Ruen ◽  
Rashmi Jha

This work reports a spiking neuromorphic architecture for associative memory simulated in a SPICE environment using recently reported gated-RRAM (resistive random-access memory) devices as synapses alongside neurons based on complementary metal-oxide semiconductors (CMOSs). The network utilizes a Verilog A model to capture the behavior of the gated-RRAM devices within the architecture. The model uses parameters obtained from experimental gated-RRAM devices that were fabricated and tested in this work. Using these devices in tandem with CMOS neuron circuitry, our results indicate that the proposed architecture can learn an association in real time and retrieve the learned association when incomplete information is provided. These results show the promise for gated-RRAM devices for associative memory tasks within a spiking neuromorphic architecture framework.


2022 ◽  
Vol 208 ◽  
pp. 114358
Author(s):  
Kunihiko Kato ◽  
Yunzi Xin ◽  
Sébastien Vaucher ◽  
Takashi Shirai

2022 ◽  
Vol 452 ◽  
pp. 214280
Author(s):  
Chengming Lou ◽  
Guanglu Lei ◽  
Xianghong Liu ◽  
Jiayue Xie ◽  
Zishuo Li ◽  
...  

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