Publications associées à Optimization of nanoelectronic systems' reliability under massive defect density using Cascaded R-fold modular redundancy

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Julian Thomas Blackwell, Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy, Isadora Alves de Salles

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

2024

Contemporary Logic Synthesis: with an Application to AQFP Circuit Optimization

Siang-Yun Lee

Electronic devices play an irreplaceable role in our lives. With the tightening time to market, exploding demand for computing power, and continuous desire for smaller, faster, less energy-consuming, and lower-cost chips, computer-aided design for electron ...

EPFL2024

Unified Harmonic Power-Flow and Stability Analysis of Power Grids with Converter-Interfaced Distributed Energy Resources

Johanna Kristin Maria Becker

Modern power distribution systems are experiencing a large-scale integration of Converter-Interfaced Distributed Energy Resources (CIDERs). Their presence complicates the analysis and mitigation of harmonics, whose creation and propagation may be amplified ...

EPFL2024

Fairness and Explainability in Clustering Problems

Xinrui Jia

In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

EPFL2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Qiang Liu, Feng Yu, Yichen Xu

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

Assoc Computing Machinery2023

RDS: FPGA Routing Delay Sensors for Effective Remote Power Analysis Attacks

Mirjana Stojilovic, Ognjen Glamocanin, David Spielmann

State-of-the-art sensors for measuring FPGA voltage fluctuations are time-to-digital converters (TDCs). They allow detecting voltage fluctuations in the order of a few nanoseconds. The key building component of a TDC is a delay line, typically implemented ...

2023

Neural ADMIXTURE for rapid genomic clustering

Albert Dominguez Mantes

Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster ...

SPRINGERNATURE2023

The nanoscale impact of individual nonradiative point defects on InGaN/GaN quantum wells

Thomas Fjord Kjaersgaard Weatherley

Today's world depends on optoelectronic devices: light-emitting diodes illuminate our houses and backlight the displays on our gadgets, while laser diodes underpin fibre-optic communication. Such optoelectronic devices rely on crystalline semiconductor het ...

EPFL2023

Improving K-means Clustering Using Speculation

Anastasia Ailamaki, Viktor Sanca, Eleni Zapridou, Stefan Igescu

K-means is one of the fundamental unsupervised data clustering and machine learning methods. It has been well studied over the years: parallelized, approximated, and optimized for different cases and applications. With increasingly higher parallelism leadi ...

2023

Energy-Efficient Electronic Functions Based on the Co-integration of 2D and Ferroelectric Materials

Sadegh Kamaei Bahmaei

The digital revolution has significantly transformed our world over the past decades, driven by the scaling of transistor dimensions and the exponential increase in computation power. However, as the CMOS scaling era approaches its end, the semiconductor i ...

EPFL2023

Improving Standard-Cell Design Flow using Factored Form Optimization

Giovanni De Micheli, Alessandro Tempia Calvino

Factored form is a powerful multi-level representation of a Boolean function that readily translates into an implementation of the function in CMOS technology. In particular, the number of literals in a factored form correlates strongly with the number of ...

IEEE2023

Zn3P2 as an earth-abundant photovoltaic material: from growth to device

Rajrupa Paul

The development of cost-effective and earth-abundant semiconducting materials is imperative for the sustainable deployment of photovoltaic technology. Zinc phosphide (Zn3P2) is a promising candidate for terawatt-scale electricity generation. It has a near- ...

EPFL2023

Nanoscale Growth Initiation as a Pathway to Improve the Earth-Abundant Absorber Zinc Phosphide

Anna Fontcuberta i Morral, Elias Zsolt Stutz, Jean-Baptiste Leran, Mahdi Zamani, Simon Robert Escobar Steinvall, Rajrupa Paul, Mirjana Dimitrievska

Growth approaches that limit the interface area between layers to nanoscale regions are emerging as a promising pathway to limit the interface defect formation due to mismatching lattice parameters or thermal expansion coefficient. Interfacial defect mitig ...

AMER CHEMICAL SOC2022

Stoichiometry modulates the optoelectronic functionality of Zinc Phosphide (Zn3-xP2+x)

Anna Fontcuberta i Morral, Elias Zsolt Stutz, Jean-Baptiste Leran, Mahdi Zamani, Maria Chiara Spadaro, Simon Robert Escobar Steinvall, Santhanu Panikar Ramanandan, Rajrupa Paul, Diego Armando Sandoval Salaiza, Mirjana Dimitrievska, Mischa Samuel Flór

Predictive synthesis–structure–property relationships are at the core of materials design for novel applications. In this regard, correlations between the compositional stoichiometry variations and functional properties are essential for enhancing the perf ...

2022

In Operando, Photovoltaic, and Microscopic Evaluation of Recombination Centers in Halide Perovskite-Based Solar Cells

Ulf Anders Hagfeldt, Henry Snaith, Silver Hamill Turren Cruz

The origin of the low densities of electrically active defects in Pb halide perovskite (HaP), a crucial factor for their use in photovoltaics, light emission, and radiation detection, remains a matter of discussion, in part because of the difficulty in det ...

AMER CHEMICAL SOC2022

Deep defects in InGaN LEDs: modeling the impact on the electrical characteristics

Jean-François Carlin, Nicolas Grandjean, Camille Haller

Deep defects have a fundamental role in determining the electro-optical characteristics and in the efficiency of InGaN light-emitting diodes (LEDs). However, modeling their effect on the electrical characteristics of the LED is not straightforward. ...

SPIE-INT SOC OPTICAL ENGINEERING2022

Single-Trace Clustering Power Analysis of the Point-Swapping Procedure in the Three Point Ladder of Cortex-M4 SIKE

Aymeric Genet, Novak Kaluderovic

In this paper, the recommended implementation of the post-quantum key exchange SIKE for Cortex-M4 is attacked through power analysis with a single trace by clustering with the k-means algorithm the power samples of all the invocations of the elliptic curve ...

Springer, Cham2022

Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap

Florent Gérard Krzakala, Lenka Zdeborová, Bruno Loureiro, Luca Pesce, Bruno Loureiro

A simple model to study subspace clustering is the high-dimensional k -Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistically optimal reconstruction error in this model i ...

2022

Covariance matrices for variance-suppressed simulations

Cheng Zhao

Cosmological N-body simulations provide numerical predictions of the structure of the Universe against which to compare data from ongoing and future surveys, but the growing volume of the Universe mapped by surveys requires correspondingly lower statistica ...

OXFORD UNIV PRESS2022