Name
Abstract | ||
|---|---|---|
Neuromorphic computing is a promising candidate for breaking the von Neumann bottleneck and developing high-efficient computing systems. Here we present a W/TaOx/Pt high-precision electronic synapse with excellent analog properties for neuromorphic computing. The device exhibits the potential of 10-bit weight precision, which is state of the art in conductance levels. Furthermore, the device shows linear weight update behavior in a specific conductance range, linear I-V curves in low voltage regime, long time retention, and precise modulation of weight. These characteristics are very helpful for improving the accuracy of neuromorphic networks. Finally, a 400 x 60 x 10 three-layer perceptron was constructed with W/TaOx/Pt synapses for MNIST classification and similar to 92% accuracy was achieved. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2961166 | IEEE ACCESS |
Keywords | DocType | Volume |
Memristor, electronic synapse, neuromorphic computing, MNIST classification | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sen Liu | 1 | 1 | 2.39 |
| Kun Li | 2 | 0 | 0.68 |
| Yi Sun | 3 | 0 | 0.34 |
| Xi Zhu | 4 | 0 | 2.03 |
| Zhiwei Li | 5 | 1315 | 107.73 |
| Bing Song | 6 | 5 | 3.82 |
| Haijun Liu | 7 | 2 | 4.43 |
| Qingjiang Li | 8 | 6 | 4.87 |