Name
Title | ||
|---|---|---|
Bansor: Improving Tensor Program Auto-Scheduling with Bandit Based Reinforcement Learning |
Abstract | ||
|---|---|---|
Efficient execution is crucial to the successful deployment of deep learning models to real-world applications. Considerable recent effort have been devoted to computer systems for automatic discovery of efficient schedules for executing tensor programs on given hardware platforms. Built on TVM [1], Ansor [2] is the most recent and state-of-the-art framework for auto-scheduling deep neural net com... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICTAI52525.2021.00045 | 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) |
Keywords | DocType | ISSN |
Tensor program scheduling,Reinforcement learning,Bandit algorithms | Conference | 1082-3409 |
ISBN | Citations | PageRank |
978-1-6654-0898-1 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Gao | 1 | 0 | 0.68 |
| Tong Mo | 2 | 0 | 0.34 |
| Taylor Zowtuk | 3 | 0 | 0.34 |
| Tanvir Sajed | 4 | 0 | 0.34 |
| Laiyuan Gong | 5 | 0 | 0.34 |
| Hanxuan Chen | 6 | 0 | 0.34 |
| Shangling Jui | 7 | 1 | 3.05 |
| Wei Lu | 8 | 0 | 0.68 |