Assistant Professor in Neuromorphic Computing & Engineering
Eindhoven University of Technology (TU/e)
I take inspiration from neural systems' extraordinary information processing abilities to design more robust, adaptable, and energy-efficient microchips. My work focuses on developing brain-inspired, ultra-low-power systems, advancing our understanding of distributed information processing, and addressing real-world challenges through innovative solutions.
Neuromorphic Edge Computing Systems
My research centers on neuromorphic computing and engineering, spanning computational model development to microelectronic architecture design. I apply brain-inspired principles to create energy-efficient sensing and computing technologies with applications in robotics, machine vision, temporal signal processing, and biomedical analysis.
Developing spiking neural network models and brain-inspired learning algorithms implemented on FPGA platforms. Exploring adaptive, recurrent architectures for time-domain classification and online learning.
FPGA SNN Learning RulesDesigning ultra-low-power ASICs and edge-AI accelerators for neuromorphic workloads. Chip design in Skywater 130nm and advanced nodes, targeting orders-of-magnitude efficiency gains over conventional processors.
Edge-AI ASIC Low PowerLeveraging event-driven cameras and sensors combined with spiking neural networks for efficient real-time processing. Applications in radar-camera fusion, gesture recognition, and biomedical signal analysis.
DVS Radar BiomedicalMicro-scale brain-inspired computing architectures for ultra-low-power edge intelligence.
Neuromorphic HardwareEvent-based vision processing with spiking neural networks for real-time object detection and tracking.
Event Camera SNNRadar signal processing with spiking neural networks for gesture recognition and human activity detection.
FMCW Radar SNNEnergy-efficient keyword spotting using spiking neural networks for always-on edge devices.
Audio Edge-AILocal learning rules for neuromorphic systems enabling on-chip adaptation without backpropagation.
Learning Rules On-chipSpiking neural network transformer architectures combining attention mechanisms with spike-based computation.
Transformer SNNOpen-source analog and mixed-signal neuromorphic chip designs using the Skywater 130nm PDK.
aVLSI Open SourceApplying spiking neural networks to genomics data analysis and biological sequence processing.
Genomics SNNExploiting spiking neural networks and event-based coding for efficient processing of biological signals (ECG, EEG) at the edge.
Biomedical ECG EEG Edge-AIHands-on demonstrations of neuromorphic computing in action.
Spiking Neural Network implementation of YOLO for energy-efficient object detection.
SNN Object Detection DemoVisualization of the spiking neural network activity during gesture classification (IBM-DVS).
SNN Visualization DemoEindhoven University of Technology (TU/e)
Department of Electrical Engineering, Electronic Systems group. Leading the NECS Lab on neuromorphic edge computing systems.
IMEC, Netherlands
Ultra-low-power systems for IoT. Designed neuromorphic processing architectures for edge computing applications.
University of Zurich & Inilabs GmbH
Neuromorphic engineering research at the Institute of Neuroinformatics. Development of event-driven sensing systems.
University of Zurich & ETH Zurich
International PhD from the ETH Neuroscience Centre Zurich, Institute of Neuroinformatics.
Sapienza University of Rome
University of Parma
Fundamentals of neural computation, spiking neural networks, and brain-inspired algorithms. Covers neuron models, learning rules, and neuromorphic hardware principles.
Design principles for intelligent hardware architectures. Topics include FPGA-based accelerators, neural network hardware implementation, and energy-efficient computing systems.
Invited Talk • Institute of Neuroinformatics, Colloquium Series • Feb 20, 2026 • Zurich, Switzerland
Download Slides (PDF, 39.1 MB)
Invited Talk • 35th International Conference on Field-Programmable Logic and Applications • Sept 2, 2025 • Leiden, Netherlands
Download Slides (PDF, 26.5 MB)
Invited Talk • BrainInspiration 2024 • Oct 17, 2024 • Groningen, Netherlands
Download Slides (PDF, 17.5 MB)
Nature Machine Intelligence., 2021
IEEE Transactions on Biomedical Circuits and Systems., 2015
Frontiers in Neuroscience., 2021
Nature Machine Intelligence., 2023
Neuromorphic Computing and Engineering., 2025
FMCW Radar & Event-Based Camera fusion dataset for aircraft marshaling signal recognition.
Radar Event Camera MultimodalJava AER — first published version of the open-source framework for address-event representation processing.
AER Open SourceUS Patent App. 17/998,532 — 2023
EP 22214325.7 — Filed Dec 16, 2022
EP 3910369A1 — Filed May 13, 2020
Federico Corradi
POBox 513, 5600 MB Eindhoven, The Netherlands
Eindhoven University of Technology
Electrical Engineering Dep.
Flux Building, Room 4.130
+31 (0)40 247 2556
Available for consulting in neuromorphic engineering, edge-AI hardware design, and spiking neural network solutions.
KvK registration: 94271437
Editorial board member, Microprocessors and Microsystems. Review editor for Frontiers in Neuromorphic Engineering, IEEE, IOP Science. Reviewer for Nature, Science, Nature Electronics, and Nature Communications.