A5064099921- Advanced Memory and Neural Computing
- Neural Networks and Reservoir Computing
- Ferroelectric and Negative Capacitance Devices
- Magnetic properties of thin films
- Wireless Signal Modulation Classification
- Non-Destructive Testing Techniques
- Quantum and electron transport phenomena
- Neural Networks and Applications
- Optical Network Technologies
- Nonlinear Dynamics and Pattern Formation
Thales (France)
2024-2025
Université Paris-Saclay
2023-2025
Centre National de la Recherche Scientifique
2023-2024
Laboratoire Albert Fert
2023
Abstract Quantum reservoir computing is a promising approach for quantum neural networks, capable of solving hard learning tasks on both classical and input data. However, current approaches with qubits suffer from limited connectivity. We propose an implementation that obtains large number densely connected neurons by using parametrically coupled oscillators instead physically qubits. analyze specific hardware based superconducting circuits: just two oscillators, we create comprising up to...
The classification of radio-frequency (RF) signals is crucial for applications in robotics, traffic control, and medical devices. Spintronic devices, which respond to RF via ferromagnetic resonance, offer a promising solution. Recent studies have shown that neural network nanoscale magnetic tunnel junctions can classify without digitization. However, the complexity these poses challenges rapid scaling. In this work, we demonstrate simple spintronic known as metallic spin-diodes, effectively...
Extracting information from radio-frequency (RF) signals using artificial neural networks at low energy cost is a critical need for wide range of applications radars to health. These RF inputs are composed multiple frequencies. Here, we show that magnetic tunnel junctions can process analog with frequencies in parallel and perform synaptic operations. Using backpropagation-free method called extreme learning, classify noisy images encoded by signals, experimental data functioning as both...
Spintronic devices have recently attracted a lot of attention in the field unconventional computing due to their non-volatility for short- and long-term memory, nonlinear fast response, relatively small footprint. Here we demonstrate experimentally how voltage driven magnetization dynamics dual free layer perpendicular magnetic tunnel junctions can emulate spiking neurons hardware. The output rate was controlled by varying dc bias across device. field-free operation this two-terminal device...
The ability to process time-series at low energy cost is critical for many applications. Recurrent neural network, which can perform such tasks, are computationally expensive when implementing in software on conventional computers. Here we propose implement a recurrent network hardware using spintronic oscillators as dynamical neurons. Using numerical simulations, build multi-layer and demonstrate that use backpropagation through time (BPTT) standard machine learning tools train this...
Spintronic devices have recently attracted a lot of attention in the field unconventional computing due to their non-volatility for short and long term memory, non-linear fast response relatively small footprint. Here we report how voltage driven magnetization dynamics dual free layer perpendicular magnetic tunnel junctions enable emulate spiking neurons hardware. The output rate was controlled by varying dc bias across device. field-free operation this two terminal device its robustness...