Dr. Federico Corradi is an Assistant Professor in the Electrical Engineering Department. His research activities are in Neuromorphic Computing and Engineering and span from the development of efficient models of computation to novel microelectronic architectures, with CMOS and emerging technologies, for both efficient deep learning and brain-inspired algorithms. His long-term research goal is to understand the principles of computation in natural neural systems and apply those for the development of a new generation of energy-efficient sensing and computing technologies. His research outputs find use in several application domains as robotics, machine vision, temporal signal processing, and biomedical signal analysis.
I take inspiration from neural systems' extraordinary information processing abilities to design more robust, adaptable, and energy-efficient microchips.
Dr. Federico Corradi is leading the Neuromorphic Edge Computing Systems Lab.
Dr. Corradi received a Ph.D. degree from the University of Zurich in Neuroinformatics and an international Ph.D. from the ETH Neuroscience Centre Zurich in 2015. He was a Postgraduate at the institute of Neuroinformatics in 2018. From 2015 to 2018, he worked in the Institute of Neuroinformatics' spin-off company Inilabs, developing event-based cameras and neuromorphic processors. From 2018 to 2022, he was at IMEC, the Netherlands, where he started a group focusing on neuromorphic ICs design activities. His passion for research recently brought him back to academia while keeping strong ties with startups and companies.
He is an active review editor of Frontiers in Neuromorphic Engineering, IEEE, and other international journals. In addition, he currently serves as a technical program committee member of several machine learning and neuromorphic symposiums and conferences (ICTOPEN, ICONS, DSD, EUROMICRO).
Accurate online training of dynamical spiking neural networks through Forward Propagation Through TimeNature Machine Intelligence (2023)
μBrainFrontiers in Neuroscience (2021)
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networksNature Machine Intelligence (2021)
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapsesFrontiers in Neuroscience (2015)
A Neuromorphic Event-Based Neural Recording System for Smart Brain-Machine-InterfacesIEEE Transactions on Biomedical Circuits and Systems (2015)