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2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

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Publications associées (40)

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Machine Learning for Modeling Stock Returns

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Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

EPFL2024

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

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Robust machine learning for neuroscientific inference

Steffen Schneider

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Biophysically accurate and machine learning-based surrogate models to optimize neuroprosthesis design and operation

Simone Romeni

Electrical stimulation of the nervous system has emerged as a promising assistive technology in case of many injuries and illnesses across various parts of the nervous system. In particular, the invasive neuromodulation of the peripheral nervous system see ...

EPFL2024

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

Reliable data-driven decision-making through optimal transport

Bahar Taskesen

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

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Advanced Silicon and SWIR Single-Photon Avalanche Diodes: Design, Simulation, and Characterization

Ekin Kizilkan

Low-level light detection with high spatial and timing accuracy is a growing area of interest by virtue of applications such as light detection and ranging (LiDAR), biomedical imaging, time-resolved Raman spectroscopy, and quantum applications. Single-phot ...

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The time-domain Cartesian multipole expansion of electromagnetic fields

Emanuela Radici, Farhad Rachidi-Haeri, Marcos Rubinstein, Nicolas Mora Parra, Elias Per Joachim Le Boudec

Time-domain solutions of Maxwell’s equations in homogeneous and isotropic media are paramount to studying transient or broadband phenomena. However, analytical solutions are generally unavailable for practical applications, while numerical solutions are co ...

Nature Publishing Group2024

Thermo-optic epsilon-near-zero effects

Camille Sophie Brès, Marco Clementi, Jiaye Wu

Nonlinear epsilon-near-zero (ENZ) nanodevices featuring vanishing permittivity and CMOS-compatibility are attractive solutions for large-scale-integrated systems-on-chips. Such confined systems with unavoidable heat generation impose critical challenges fo ...

Nature Portfolio2024

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Edoardo Charbon, Claudio Bruschini, Andrei Ardelean, Paul Mos, Yang Lin

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

Nature Portfolio2024

Emulating Multimemristive Behavior of Silicon Nanowire-Based Biosensors by Using CMOS-Based Implementations

Kapil Bhardwaj, Sandro Carrara, Junrui Chen

The research presented in this article draws inspiration from previous efforts aimed at replicating the functions of various solid-state memristors using a variety of materials. The memristor circuit emulator serves as a cost-effective tool for circuit des ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Mattia Atzeni

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

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Optimizing Dynamic Aperture Studies with Active Learning

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Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

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3D diffractive optics for linear interconnects and nonlinear processing

Niyazi Ulas Dinç

The optical domain presents potential avenues for enhancing both computing and communication due to its inherent properties of bandwidth, parallelism, and energy efficiency. This research focuses on harnessing 3-Dimensional (3D) diffractive optics for nove ...

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33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

Mahsa Shoaran, Uisub Shin, Mohammad Ali Shaeri, Amitabh Yadav

Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

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A modular multiple frequency inductive link to wirelessly power multiple miniaturized implants

Sandro Carrara, Diego Ghezzi, Gian Luca Barbruni

A modular multiple frequency coils inductive link system to wirelessly provide power for at least a medical implant at an output of a receiving coil, whereby the receiving coil is configured to be implanted in an organism. The modular multiple frequency co ...

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Drone‐Based Solar Cell Inspection With Autonomous Deep Learning

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Regular inspection and maintenance are crucial for ensuring the optimal performance of solar panels. However, conventional manual methods can be laborious, time consuming, and expensive, especially for large and inaccessible installations. Aerial inspectio ...

Wiley2024

Topics in statistical physics of high-dimensional machine learning

Hugo Chao Cui

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

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Lithium tantalate photonic integrated circuits for volume manufacturing

Tobias Kippenberg, Johann Emmeram Riemensberger, Mikhail Churaev, Xinru Ji, Zihan Li, Terence Albert Emile Blésin, Chengli Wang, Junyin Zhang, Xi Wang, Chen Yang, Wil Kao, Alisa Davydova

Electro-optical photonic integrated circuits (PICs) based on lithium niobate (LiNbO3) have demonstrated the vast capabilities of materials with a high Pockels coefficient1,2. They enable linear and high-speed modulators operating at complementary metal–oxi ...

2024