Name
Abstract | ||
|---|---|---|
Neuromorphic computing is an attractive computation paradigm with the features of massive parallelism, adaptivity to the complex input information, and tolerance to errors. As one of the most crucial components in a neuromorphic system, the electronic synapse requires high device integration density and low-energy consumption. Oxide-based resistive switching devices (RRAM) have emerged as the leading candidate to realize the synapse functions due to the extra-low energy loss per spike. This work will address the design and optimization of oxide-based RRAM synaptic devices and the impacts of the synaptic devices parameters on the performance of neuromorphic visual system. Possible solutions are also provided to suppress the intrinsic variation of the oxide-RRAM based synaptic devices to achieve high recognition accuracy and efficiency of neuromorphic visual systems. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICDSP.2015.7252074 | Digital Signal Processing |
Keywords | Field | DocType |
neural nets,resistive RAM,RRAM based synaptic device,electronic synapse,neuromorphic computing,neuromorphic visual systems,oxide based resistive switching device,synaptic devices parameter,Neural Cell,Neuromorphic Computing,RRAM,Synapse | Energy loss,Computer vision,Resistive switching,Massively parallel,Computer science,Neuromorphic engineering,Electronic engineering,Artificial intelligence,Computer hardware,Computation,Resistive random-access memory | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinfeng Kang | 1 | 55 | 10.02 |
| Gao B | 2 | 44 | 13.39 |
| Huang, P. | 3 | 31 | 5.61 |
| Liu, L.F. | 4 | 0 | 0.34 |
| X. Y. Liu | 5 | 3 | 0.71 |
| H. Y. Yu | 6 | 0 | 1.35 |
| Shimeng Yu | 7 | 490 | 56.22 |
| H.-S. Philip Wong | 8 | 645 | 106.40 |