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IEEE Symposium on VLSI Technology · 2020 · Conference paper

A SiOx, RRAM-based hardware with spike frequency adaptation for power-saving continual learning in convolutional neural networks

Irene Muñoz-Martín, S. Bianchi, Erika Covi, G. Piccolboni, Alessandro Bricalli, Amir Regev, J. F. Nodin, E. Nowak, G. Molas, Daniele Ielmini

Politecnico di Milano · Commissariat à l'Énergie Atomique et aux Énergies Alternatives · CEA Grenoble · Laboratoire d'Électronique des Technologies de l'Information

Publisher page ↗
Published in
2020 IEEE Symposium on VLSI Technology
Date
2020-06-01
Citations
8
Cite
@inproceedings{muozmartn2020siox,
  title = {A SiOx, RRAM-based hardware with spike frequency adaptation for power-saving continual learning in convolutional neural networks},
  author = {Irene Muñoz-Martín and S. Bianchi and Erika Covi and G. Piccolboni and Alessandro Bricalli and Amir Regev and J. F. Nodin and E. Nowak and G. Molas and Daniele Ielmini},
  year = {2020},
  booktitle = {2020 IEEE Symposium on VLSI Technology},
  doi = {10.1109/VLSITechnology18217.2020.9265072},
  url = {https://doi.org/10.1109/VLSITechnology18217.2020.9265072},
}