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Publications associées (514)

Front-End Circuits for Radiation-Hard Monolithic CMOS Sensors targeting High-Energy Physics Applications

Monolithic pixel sensors integrate the sensor matrix and readout in the same silicon die, and therefore present several advantages over the more largely used hybrid detectors in high-energy physics. They offer an easier detector assembly, lower cost, lower ...

EPFL2023

Monitoring of Protein and pH Levels in Human Biofluids using FD-SOI Silicon Nanowire Arrays

Luca Capua

Medical research and technological advancements are heading towards tailored healthcare approaches that prioritize individual needs, allowing for more accurate diagnoses, more effective treatments, and better patient outcomes overall. One such approach is ...

EPFL2023

From the Graphical Processing Unit (GPU) to computing power: exploring the situated practices of liquid nitrogen overclocking

Drawing from a fieldwork conducted at COMPUTEX Taipei, one of the largest computer expo in the world, this contribution proposes to zoom-in at the level of Graphical Processing Units (GPU) manufacturers and their interactions with computer hardware hobbyis ...

2023

A Compact Front-End Circuit for a Monolithic Sensor in a 65-nm CMOS Imaging Technology

Edoardo Charbon, Francesco Piro

This article presents the design of a front-end circuit for monolithic active pixel sensors (MAPSs). The circuit operates with a sensor featuring a small, low-capacitance (

Piscataway2023

Tradeoffs in Low-Power Accelerators Design for Large-Scale Interferometers

David Atienza Alonso, Miguel Peon Quiros, Denisa-Andreea Constantinescu, Benoît Walter Denkinger

Large-scale scientific infrastructures like SKAO—the world’s largest radio observatory for the coming decades—are generating massive-scale data streams of multi-Tb/s to be processed using complex interferometry algorithms. Concretely, SKAO is expected to g ...

2023

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Florent Gérard Krzakala, Julien Marcel Daniel Emmanuel Launay

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring resu ...

IEEE2023

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

Kathrin Grosse, Sebastiano Vascon

The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that ...

2023

Portable impedance-sensing device for microorganism characterization in the field

Sandro Carrara, Karim Bouzid

A variety of biosensors have been proposed to quickly detect and measure the properties of individual microorganisms among heterogeneous populations, but challenges related to cost, portability, stability, sensitivity, and power consumption limit their app ...

NATURE PORTFOLIO2023

Backpropagation-free training of deep physical neural networks

Romain Christophe Rémy Fleury, Ali Momeni, Matthieu Francis Malléjac, Babak Rahmani, Marc Philipp Del Hougne

Recent successes in deep learning for vision and natural language processing are attributed to larger models but come with energy consumption and scalability issues. Current training of digital deep-learning models primarily relies on backpropagation that ...

2023

SEKV-E: Parameter Extractor of Simplified EKV I-V Model for Low-Power Analog Circuits

Christian Enz, Hung-Chi Han, Antonio D'Amico

This paper presents the open-source Python-based parameter extractor (SEKV-E) for the simplified EKV (sEKV) model, which enables the modern low-power circuit designs with the inversion coefficient design methodology. The tool extracts the essential sEKV pa ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

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