Name
Abstract | ||
|---|---|---|
Designing deep neural network models for embedded devices is a challenging task since the models need to be lightweight, fast, and accurate. This paper proposes a hardware-aware model optimization tool (HOT) to optimize a given model in terms of latency or accuracy by replacing its existing operators with the best-performing operators for target hardware. The proposed tool finds optimal operators ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICMEW53276.2021.9456004 | 2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) |
Keywords | DocType | ISSN |
Pose estimation,Neural networks,Object detection,Detectors,Tools,Network architecture,Hardware | Conference | 2330-7927 |
ISBN | Citations | PageRank |
978-1-6654-4989-2 | 0 | 0.34 |
References | Authors | |
0 | 11 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cagri Ozcinar | 1 | 0 | 0.34 |
| Dongsung Kim | 2 | 0 | 0.34 |
| Benjamin Rufus Duckworth | 3 | 0 | 0.34 |
| Shayan Joya | 4 | 0 | 0.34 |
| Nicolas Scotto Di Perto | 5 | 0 | 0.34 |
| Attila Dusnoki | 6 | 0 | 0.34 |
| Márkó Fabó | 7 | 0 | 0.34 |
| Dániel Vince | 8 | 0 | 0.68 |
| Gábor Lóki | 9 | 0 | 1.01 |
| Ákos Kiss | 10 | 0 | 0.34 |
| Christopher Alder | 11 | 0 | 0.34 |