Name
Abstract | ||
|---|---|---|
Random Forests (RFs) are widely used Machine Learning models in low-power embedded devices, due to their hardware friendly operation and high accuracy on practically relevant tasks. The accuracy of a RF often increases with the number of internal weak learners (decision trees), but at the cost of a proportional increase in inference latency and energy consumption. Such costs can be mitigated consi... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/VLSI-SoC53125.2021.9606986 | 2021 IFIP/IEEE 29th International Conference on Very Large Scale Integration (VLSI-SoC) |
Keywords | DocType | ISSN |
Embedded Systems,Machine Learning | Conference | 2021 IFIP/IEEE 29th International Conference on Very Large Scale
Integration (VLSI-SoC), 2021, pp. 1-6 |
ISBN | Citations | PageRank |
978-1-6654-2614-5 | 3 | 0.47 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Daghero | 1 | 4 | 1.86 |
| Alessio Burrello | 2 | 6 | 6.01 |
| Chen Xie | 3 | 4 | 1.52 |
| Luca Benini | 4 | 13116 | 1188.49 |
| Andrea Calimera | 5 | 293 | 38.89 |
| Enrico Macii | 6 | 2405 | 349.96 |
| Massimo Poncino | 7 | 460 | 57.48 |
| daniele jahier pagliari | 8 | 21 | 13.19 |