1. Zhang, Y., Gomony, M. D., Corporaal, H., & Corradi, F. (2024, October). A Scalable Hardware Architecture for Efficient Learning of Recurrent Neural Networks at the Edge. In 2024 IFIP/IEEE 32nd International Conference on Very Large Scale Integration (VLSI-SoC) (pp. 1-4). IEEE. PDF available via IEEE & poster available here!

2. Yin B. & Corradi, F. (2025).  Never Reset Again: A Mathematical Framework for Continual Inference in Recurrent Neural Networks. To appear in Neuro-Inspired Computational Elements (NICE) conference, Heidelberg, March 2025. arXiv preprint arXiv:2412.15983 (2024). PDF available here.

3. Zhang, Y., Yin B., Gomony, M. D., Corporaal, H., Trinitis C. &  Corradi, F. Hardware/Software Co-Design Optimization for Training Recurrent Neural Networks at the Edge. In 2025 Journal of Low-Power Electronics and Applications (JLPEA) 2025, 15, 15. https://doi.org/10.3390/jlpea15010015. PDF available via link (open access)

4. Xun, H., Fieback, M., Yuan, S., Wang, C., Hua, E., Bolzani Poehls, L., Aziza, H., Cantoro, R., Taouil, M. & Hamdioui, S. In-Field Monitoring and Preventing Read Disturb Faults in RRAMs. Accepted at European Test Symposium (ETS), Tallinn, May 2025.