Name
Abstract | ||
|---|---|---|
Thanks to the enormous computing power of GPUs, Machine Learning (ML) based on artificial neural networks has found its way into many important application fields. Sophisticated compiler infrastructures facilitate the task of mapping neural networks onto these accelerators. Recently, new developments have also led to compilation and design automation flows that target FPGA-based accelerators. Alth... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/FCCM51124.2021.00010 | 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) |
Keywords | DocType | ISSN |
Measurement,Design automation,Neural networks,Machine learning,Tools,Licenses,Resource management | Conference | 2576-2613 |
ISBN | Citations | PageRank |
978-1-6654-3555-0 | 2 | 0.37 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick Plagwitz | 1 | 2 | 0.37 |
| Frank Hannig | 2 | 595 | 75.66 |
| Martin Ströbel | 3 | 2 | 0.37 |
| Christoph Strohmeyer | 4 | 2 | 0.71 |
| Juergen Teich | 5 | 90 | 18.01 |