Name
Abstract | ||
|---|---|---|
In the last few years, research and development on Deep Learning models & techniques for ultra-low-power devices– in a word, TinyML – has mainly focused on a train-then-deploy assumption, with static models that cannot be adapted to newly collected data without cloud-based data collection and fine-tuning. Latent Replay-based Continual Learning (CL) techniques ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JETCAS.2021.3121554 | IEEE Journal on Emerging and Selected Topics in Circuits and Systems |
Keywords | DocType | Volume |
Task analysis,Microcontrollers,Memory management,Costs,Deep learning,Data models,Adaptation models | Journal | 11 |
Issue | ISSN | Citations |
4 | 2156-3357 | 3 |
PageRank | References | Authors |
0.47 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonardo Ravaglia | 1 | 3 | 0.47 |
| Manuele Rusci | 2 | 15 | 3.82 |
| Davide Nadalini | 3 | 3 | 0.47 |
| Alessandro Capotondi | 4 | 39 | 8.25 |
| Francesco Conti 0001 | 5 | 125 | 18.24 |
| Luca Benini | 6 | 13116 | 1188.49 |