744 citations · 5.9k across the 174 of their papers we have counts for
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Efficiency measurements of GEM GE1/1 chambers in the upgraded CMS Endcap Muon System using 2023 collision data at TeV
M. Abbas, S. Abbott, M. Abbrescia +184
The CMS experiment at the Large Hadron Collider employs Gas Electron Multiplier (GEM) detectors, a technology based on gaseous ionization, as one of the muon detectors. The muon sp…
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…
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…
APEIRON: composing smart TDAQ systems for high energy physics experiments
Roberto Ammendola, Andrea Biagioni, Carlotta Chiarini +12
We present APEIRON, a distributed heterogeneous processing framework comprising both hardware architecture and software stack for multi-FPGA systems. Targeting smart trigger and da…
Cherenkov and scintillation light separation in BGO and BSO crystals coupled to SiPMs for dual-readout electromagnetic calorimetry at future colliders
M. Alviggi, B. Argiento, E. Auffray +29
We report on the separation of Cherenkov and scintillation light in BGO and BSO crystals read out with silicon photomultipliers (SiPMs). The two light components are disentangled o…
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…
Unsupervised sleep-like intra- and inter-layer plasticity categorizes and improves energy efficiency in a multilayer spiking network
Leonardo Tonielli, Cosimo Lupo, Elena Pastorelli +4
Sleep is thought to support memory consolidation and the recovery of optimal energetic regime by reorganizing synaptic connectivity, yet how plasticity across hierarchical brain ci…