24 citations · 98 across the 27 of their papers we have counts for
28 papers
ePIC Early Science Report
D. Abbott, N. Abdelrahman, S. Abhijit +781
This Early Science Report from the ePIC Collaboration outlines the compelling physics program achievable during the first years of operation of the Electron-Ion Collider (EIC), pri…
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…
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…
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…
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…
APEIRON: composing smart TDAQ systems for high energy physics experiments
Roberto Ammendola, Andrea Biagioni, Carlotta Chiarini +11
APEIRON is a framework encompassing the general architecture of a distributed heterogeneous processing platform and the corresponding software stack, from the low level device driv…
Architectural improvements and technological enhancements for the APEnet+ interconnect system
R. Ammendola, A. Biagioni, O. Frezza +9
The APEnet+ board delivers a point-to-point, low-latency, 3D torus network interface card. In this paper we describe the latest generation of APEnet NIC, APEnet v5, integrated in a…
Real-time cortical simulations: energy and interconnect scaling on distributed systems
Francesco Simula, Elena Pastorelli, Pier Stanislao Paolucci +11
We profile the impact of computation and inter-processor communication on the energy consumption and on the scaling of cortical simulations approaching the real-time regime on dist…
Large Scale Low Power Computing System - Status of Network Design in ExaNeSt and EuroExa Projects
Roberto Ammendola, Andrea Biagioni, Fabrizio Capuani +10
The deployment of the next generation computing platform at ExaFlops scale requires to solve new technological challenges mainly related to the impressive number (up to 10^6) of co…
The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States
Roberto Ammendola, Andrea Biagioni, Fabrizio Capuani +10
Efficient brain simulation is a scientific grand challenge, a parallel/distributed coding challenge and a source of requirements and suggestions for future computing architectures.…
Gaussian and exponential lateral connectivity on distributed spiking neural network simulation
Elena Pastorelli, Pier Stanislao Paolucci, Francesco Simula +10
We measured the impact of long-range exponentially decaying intra-areal lateral connectivity on the scaling and memory occupation of a distributed spiking neural network simulator…
GPU-based Real-time Triggering in the NA62 Experiment
R. Ammendola, A. Biagioni, P. Cretaro +17
Over the last few years the GPGPU (General-Purpose computing on Graphics Processing Units) paradigm represented a remarkable development in the world of computing. Computing for Hi…
Impact of exponential long range and Gaussian short range lateral connectivity on the distributed simulation of neural networks including up to 30 billion synapses
Elena Pastorelli, Pier Stanislao Paolucci, Roberto Ammendola +7
Recent experimental neuroscience studies are pointing out the role of long-range intra-areal connectivity that can be modeled by a distance dependent exponential decay of the synap…
Scaling to 1024 software processes and hardware cores of the distributed simulation of a spiking neural network including up to 20G synapses
Elena Pastorelli, Pier Stanislao Paolucci, Roberto Ammendola +7
This short report describes the scaling, up to 1024 software processes and hardware cores, of a distributed simulator of plastic spiking neural networks. A previous report demonstr…
Power, Energy and Speed of Embedded and Server Multi-Cores applied to Distributed Simulation of Spiking Neural Networks: ARM in NVIDIA Tegra vs Intel Xeon quad-cores
Pier Stanislao Paolucci, Roberto Ammendola, Andrea Biagioni +7
This short note regards a comparison of instantaneous power, total energy consumption, execution time and energetic cost per synaptic event of a spiking neural network simulator (D…
NaNet: a Low-Latency, Real-Time, Multi-Standard Network Interface Card with GPUDirect Features
A. Lonardo, F. Ameli, R. Ammendola +14
While the GPGPU paradigm is widely recognized as an effective approach to high performance computing, its adoption in low-latency, real-time systems is still in its early stages. A…
NaNet:a low-latency NIC enabling GPU-based, real-time low level trigger systems
Roberto Ammendola, Andrea Biagioni, Riccardo Fantechi +13
We implemented the NaNet FPGA-based PCI2 Gen2 GbE/APElink NIC, featuring GPUDirect RDMA capabilities and UDP protocol management offloading. NaNet is able to receive a UDP input da…
Architectural improvements and 28 nm FPGA implementation of the APEnet+ 3D Torus network for hybrid HPC systems
Roberto Ammendola, Andrea Biagioni, Ottorino Frezza +7
Modern Graphics Processing Units (GPUs) are now considered accelerators for general purpose computation. A tight interaction between the GPU and the interconnection network is the…
NaNet: a flexible and configurable low-latency NIC for real-time trigger systems based on GPUs
R. Ammendola, A. Biagioni, O. Frezza +10
NaNet is an FPGA-based PCIe X8 Gen2 NIC supporting 1/10 GbE links and the custom 34 Gbps APElink channel. The design has GPUDirect RDMA capabilities and features a network stack pr…
Distributed simulation of polychronous and plastic spiking neural networks: strong and weak scaling of a representative mini-application benchmark executed on a small-scale commodity cluster
Pier Stanislao Paolucci, Roberto Ammendola, Andrea Biagioni +7
We introduce a natively distributed mini-application benchmark representative of plastic spiking neural network simulators. It can be used to measure performances of existing compu…
A heterogeneous many-core platform for experiments on scalable custom interconnects and management of fault and critical events, applied to many-process applications: Vol. II, 2012 technical report
Roberto Ammendola, Andrea Biagioni, Ottorino Frezza +9
This is the second of a planned collection of four yearly volumes describing the deployment of a heterogeneous many-core platform for experiments on scalable custom interconnects a…
'Mutual Watch-dog Networking': Distributed Awareness of Faults and Critical Events in Petascale/Exascale systems
Roberto Ammendola, Andrea Biagioni, Ottorino Frezza +7
Many tile systems require techniques to be applied to increase components resilience and control the FIT (Failures In Time) rate. When scaling to peta- exa-scale systems the FIT ra…