Name
Abstract | ||
|---|---|---|
Neuromorphic computing can potentially solve the von Neumann bottleneck of current mainstream computing because it excels at self-adaptive learning and highly parallel computing and consumes much less energy. Synaptic devices that mimic biological synapses are critical building blocks for neuromorphic computing. Inspired by recent progress in optogenetics and visual sensing, light has been increasingly incorporated into synaptic devices. This paves the way to optoelectronic synaptic devices with a series of advantages such as wide bandwidth, negligible resistance-capacitance (RC) delay and power loss, and global regulation of multiple synaptic devices. Herein, the basic functionalities of synaptic devices are introduced. All kinds of optoelectronic synaptic devices are then discussed by categorizing them into optically stimulated synaptic devices, optically assisted synaptic devices, and synaptic devices with optical output. Existing practical scenarios for the application of optoelectronic synaptic devices are also presented. Finally, perspectives on the development of optoelectronic synaptic devices in the future are outlined. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1002/aisy.202000099 | ADVANCED INTELLIGENT SYSTEMS |
Keywords | DocType | Volume |
artificial neural networks, neuromorphic computing, optoelectronic synaptic devices | Journal | 3 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Wang | 1 | 0 | 0.34 |
| Lei Yin | 2 | 0 | 0.68 |
| Wen Huang | 3 | 0 | 0.34 |
| Yayao Li | 4 | 0 | 0.34 |
| Shijie Huang | 5 | 0 | 0.34 |
| Yiyue Zhu | 6 | 0 | 0.68 |
| Deren Yang | 7 | 0 | 0.34 |
| Xiaodong Pi | 8 | 0 | 1.01 |