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.

5. F. Corradi, A. Zjajo, L. M. B. Poehls, M. Krstić, O. Moreira, Z. Zhu, F. Merchant, Neuromorphic Edge Computing: Challenges, Opportunities, and Current Solutions, IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED 2025), University of Iceland, Reykjavik, Iceland.

6. T. Spyrou, H.-G. Stratigopoulos, I. Alouani, S. Hamdioui and A. Gebregiorgis, On the Trustworthiness of Spiking Neural Networks and Neuromorphic Systems, 2025 IEEE European Test Symposium (ETS), Tallinn, Estonia, 2025, pp. 1-10. DOI available here.

7. H. Xun et al., In-Field Monitoring and Preventing Read Disturb Faults in ReRAMs, 2025 IEEE European Test Symposium (ETS), Tallinn, Estonia, 2025, pp. 1-6. DOI available here.

8. A. Gebregiorgis, A. Yousefzadeh, S. Eissa, M. Ali Siddiqi, C. Frenkel, F. Zenke, S. Bohte, A. Nael Mahmoud, A. Das, S. Hamdioui, H. Corporaal, and F. Corradi, Spike-based neuromorphic computing: An overview from bio-inspiration to hardware architectures and learning mechanisms, Microprocessors and Microsystems, 2026. DOI available here.

9. Koopman, R., Otte, S., & Bohte, S. (2026). SpikingGamma: Temporally precise online SNN training through smoothed temporal delays. The Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS 2026).

10. Surendra Hemaram, Said Hamdioui, and A. Gebregiorgis, ECC-Assisted Fault Tolerance in Memristive Crossbar-based CIM Architectures: An Overview, accepted at IEEE VLSI-SoC 2026.

11. Emmanouil Arapidis, Theofilos Spyrou, Konstantinos Stavrakakis, Emmanouil Anastasios Serlis, Moritz Fieback, Said Hamdioui, Analysis and Mitigation of IR Drop in Memristor-based AI Hardware Accelerators, 2026 Design, Automation & Test in Europe Conference (DATE). DOI available here.

12. Konstantinos Stavrakakis, Bas Smeele, Emmanouil Arapidis, Theofilos Spyrou, Anteneh Gebregiorgis, Stephan Wong, Georgi Gaydadjiev, Said Hamdioui, X-Sim: An Accurate and Scalable Simulator for Memristive Computing-in-Memory Accelerators, 2026 Design, Automation & Test in Europe Conference (DATE). DOI available here.