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-AIEindhoven 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.
Nature Machine Intelligence, 2021
Frontiers in Neuroscience, 2021
Nature Machine Intelligence, 2023
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
Flux Building, Room 4.130
Eindhoven University of Technology
+31 (0)40 247 2556
Available for consulting in neuromorphic engineering, edge-AI hardware design, and spiking neural network solutions.
KvK registration: 94271437
Review editor for Frontiers in Neuromorphic Engineering, IEEE, IOP Science. Editorial board member, Microprocessors and Microsystems.