most citedRadiation tolerance tests on key components of the ePIC-dRICH readout card

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

physics.ins-det2026

Online Data Reduction with Spiking Neural Networks: A Temporal-Coincidence Encoder and Distributed SNN for the ePIC dRICH Detector

Pierpaolo Perticaroli, Roberto Ammendola, Andrea Biagioni +10

The dual-radiator Ring Imaging Cherenkov (dRICH) detector of the ePIC experiment at the Electron-Ion Collider (EIC) will read out 320,000 silicon photomultiplier (SiPM) chann…

cs.AR2026

AIGOR: A Modular, Event-Driven Neuromorphic Architecture for Configurable SNN Inference

Pierpaolo Perticaroli, Roberto Ammendola, Andrea Biagioni +10

Spiking neural networks (SNNs) run today on a fragmented landscape of hardware: dedicated neuromorphic processors, application-specific FPGA accelerators, and large-scale neuroscie…

physics.ins-det20261 cited

Radiation tolerance tests on key components of the ePIC-dRICH readout card

S. Geminiani, B. R. Achari, N. Agrawal +60

The dual-radiator RICH (dRICH) detector of the ePIC experiment will employ over 300000 SiPM pixels as photosensors, organized into more than 1000 Photon Detection Units. Each PDU i…

cs.NI2026

NET4EXA: Pioneering the Future of Interconnects for Supercomputing and AI

Michele Martinelli, Roberto Ammendola, Andrea Biagioni +41

NET4EXA aims to develop a next-generation high-performance interconnect for HPC and AI systems, addressing the increasing demands of large-scale infrastructures, such as those requ…

physics.med-ph2025

Real-Time Motion Correction in Magnetic Resonance Spectroscopy: AI solution inspired by fundamental science

Benedetta Argiento, Alberto Annovi, Silvia Capuani +18

Magnetic Resonance Spectroscopy (MRS) is a powerful non-invasive tool for metabolic tissue analysis but is often degraded by patient motion, limiting clinical utility. The RECENTRE…