Name
Abstract | ||
|---|---|---|
We consider a model-distributed learning framework in which layers of a deep learning model is distributed across multiple workers. To achieve consistent gradient updates during the training phase, model-distributed learning requires the storage of multiple versions of the layer parameters at every worker. In this paper, we design mcPipe to reduce the memory cost of model-distributed learning, whi... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LANMAN52105.2021.9478829 | 2021 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN) |
Keywords | DocType | ISBN |
Training,Learning systems,Performance evaluation,Metropolitan area networks,Computational modeling,Neural networks,Memory management | Conference | 978-1-6654-4579-5 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengzhen Li | 1 | 0 | 0.68 |
| Hulya Seferoglu | 2 | 426 | 28.46 |
| Venkat R. Dasari | 3 | 0 | 0.34 |
| Erdem Koyuncu | 4 | 0 | 1.35 |