Name
Abstract | ||
|---|---|---|
Deep learning (DL) has achieved state-of-the-art performance in many challenging problems. However, DL requires powerful hardware for both training and deployment, increasing the cost and energy requirements and rendering large-scale applications especially difficult. Recognizing these difficulties, several neuromorphic hardware solutions have been proposed, including photonic hardware that can pr... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TETCI.2019.2923001 | IEEE Transactions on Emerging Topics in Computational Intelligence |
Keywords | DocType | Volume |
Hardware,Training,Biological neural networks,Photonics,Neurons,Neuromorphics,Computer architecture | Journal | 5 |
Issue | ISSN | Citations |
3 | 2471-285X | 1 |
PageRank | References | Authors |
0.48 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. Passalis | 1 | 117 | 33.70 |
| George Mourgias-Alexandris | 2 | 2 | 4.34 |
| Apostolos Tsakyridis | 3 | 3 | 4.63 |
| Nikos Pleros | 4 | 25 | 23.69 |
| Anastasios Tefas | 5 | 2055 | 177.05 |