Alessio Colucci

Univ.Ass. Dott.mag. / BSc

Alessio Colucci

Role

PreDoc Researcher
Embedded Computing Systems, E191-02

Contact

Publications

Towards Transient Fault Mitigation Techniques Optimized for Compressed Neural Networks / Colucci, A. (2023). Towards Transient Fault Mitigation Techniques Optimized for Compressed Neural Networks. In 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S) (pp. 211–213). IEEE. https://doi.org/10.1109/DSN-S58398.2023.00059
RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks / Marchisio, A., De Marco, A., Colucci, A., Martina, M., & Shafique, M. (2023). RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks. In 2023 International Joint Conference on Neural Networks (IJCNN). 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia. IEEE. https://doi.org/10.1109/IJCNN54540.2023.10190994
enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks / Colucci, A., Steininger, A., & Shafique, M. (2022). enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks. In Proceedings 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5155–5162). https://doi.org/10.1109/IROS47612.2022.9982181
NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks / Ahmadi, M. M., Alrahis, L., Colucci, A., Sinanoglu, O., & Shafique, M. (2022). NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks. In Proceedings 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 01–10). https://doi.org/10.1109/IJCNN55064.2022.9892545
MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences / Colucci, A., Juhasz, D., Mosbeck, M., Marchisio, A., Rehman, S., Kreutzer, M., Nadbath, G., Jantsch, A., & Shafique, M. (2021). MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences. In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). 2021 Design, Automation & Test in Europe, Online, Unknown. https://doi.org/10.23919/date51398.2021.9474158
FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks / Marchisio, A., Bussolino, B., Colucci, A., Hanif, M. A., Martina, M., Masera, G., & Shafique, M. (2020). FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks. In Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. http://hdl.handle.net/20.500.12708/55582
Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks / Marchisio, A., Bussolino, B., Colucci, A., Martina, M., Masera, G., & Shafique, M. (2020). Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks. In Proceedings of 2020 57th ACM/IEEE Design Automation Conference (DAC) (pp. 1–6). IEEE. http://hdl.handle.net/20.500.12708/55581
A Fast Design Space Exploration Framework for the Deep Learning Accelerators: Work-in-Progress / Colucci, A., Marchisio, A., Bussolino, B., Mrazek, V., Martina, M., Masera, G., & Shafique, M. (2020). A Fast Design Space Exploration Framework for the Deep Learning Accelerators: Work-in-Progress. In 2020 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS). 2020 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ ISSS), Virtual Conference, Austria. IEEE. https://doi.org/10.1109/codesisss51650.2020.9244038

Supervisions

Modeling resource utilization for spiking neural networks in FPGAs / Müllner, M. (2024). Modeling resource utilization for spiking neural networks in FPGAs [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.94824
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