NG
Nikhil Garg
University of Groningen, ETH Zurich, Université de Sherbrooke, University of Lille, BITS Pilani, Goa Campus
Employment
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University of Groningen Postdoc researcher2026 - Present
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ETH Zurich Post-doctoral researcher2025 - 2026
Education
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Université de Sherbrooke Ph.D. (Electrical Engineering)2021 - 2024
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University of Lille Ph.D. (Electronics, Microelectronics, Nanotechnology and Microwaves)2021 - 2024
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BITS Pilani, Goa Campus B.Eng. (Electrical and Electronics Engineering)? - 2021
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BITS Pilani, Goa Campus M.Sc. (Biological Sc.)? - 2021
Projects & Funding
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Ferroelectric Superlattices for Bio-Inspired Computing (ROSUBIO) Swiss National Science Foundation2024 - 2029
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Vision transformers with ferroelectric oxides European Commission2024 - 2027
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Stochastic Spiking Wireless Multimodal Sensory Systems European Research Council2024 - 2029
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Unsupervised spiking neural networks with analog memristive devices for edge computing – UNICO Agence Nationale de la Recherche2019 - 2022
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An iono-electronic neuromorphic interface for communication with living systems European Research Council2018 - 2024
Publications (36)
- Personalized spiking neural networks with ferroelectric synapses for EEG signal processing Save
- Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses Save
- Analog Weight Update Rule in Ferroelectric Hafnia, using picoJoule Programming Pulses Save
- Personalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processing Save
- Analog Weight Update Rule in Ferroelectric Hafnia, Using picoJoule Programming Pulses Save
- All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Save
- All-in-One Analog AI Hardware: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices Save
- Energy-convergence trade off for the training of neural networks on bio-inspired hardware Save
- Energy-convergence trade off for the training of neural networks on bio-inspired hardware Save
- Unsupervised local learning based on voltage-dependent synaptic plasticity for resistive and ferroelectric synapses Save