Name
Abstract | ||
|---|---|---|
Recent artificial neural network architectures use memristors to store synaptic weights. The crossbar structure of memristors is used because of its dense structure and extreme parallelism. Transistor aging impacts their computational accuracy. An enhancement of the memristor-based neural network architecture is introduced using built-in current-based calibration circuit. It is shown experimentally that the proposed approach alleviates the cell aging effect. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3232195.3232208 | NANOARCH'18: PROCEEDINGS OF THE 14TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON NANOSCALE ARCHITECTURES |
Keywords | Field | DocType |
Neural network,memristor,synapse,synaptic current,aging,current calibration | Memristor,Computer science,Aging effect,Neural network architecture,Transistor aging,Electronic engineering,Artificial neural network,Transistor,Crossbar switch | Conference |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seyed Nima Mozaffari | 1 | 6 | 1.84 |
| Krishna Prasad Gnawali | 2 | 0 | 0.34 |
| Spyros Tragoudas | 3 | 625 | 88.87 |