Retina

The INFN-RETINA is an R&D project aimed at developing and implementing a specialized processor allowing the reconstruction of events with hundreds of charged-particle tracks in pixel and silicon strip detectors, at 40 MHz, thus suitable for processing HL-LHC events at the full crossing frequency. For this purpose we design and test a new massively parallel pattern-recognition architecture, based on the so called “artificial retina algorithm”, inspired by studies of the processing of visual images by the brain as it happens in nature. We find that high-quality tracking in large detectors is possible with sub-microseconds latencies when this algorithm is implemented in modern, high-speed, high-bandwidth FPGA devices. This opens a possibility of making track reconstruction happen transparently as part of the detector readout. The RETINA project is currently an official R&D effort within the Real Time Analysis project of the LHCb Collaboration.

Papers and proceedings

  • 2021 - G. Bassi et al., A real-time FPGA-based cluster finding algorithm for LHCb silicon pixel detector, CHEP 2021. EPJ Web of Conferences 251, 04016 (2021).

  • 2020 - F. Lazzari et al., An optical network for accelerating real-time tracking with FPGAs, CTD 2020, Princeton (USA)/Virtual. Link here.

  • 2019 - G. Tuci et al., Reconstruction of track candidates at the LHC crossing rate using FPGAs, EPJ Web Conf., 245 (2020) 10001, CHEP 2019, Adelaide (Australia). Link here.

  • 2019 - G. Punzi et al., Real-time reconstruction of pixel vertex detectors with FPGAs, PoS(Vertex2019)047, Vertex 2019, Lopud (Croatia). Link here.

  • 2019 - M.J. Morello et al., Real-time reconstruction of long-lived particles at LHCb using FPGAs, ACAT 2019, Saas Fee (Switzerland). Link here.

  • 2019 - F. Lazzari et al., Real-time cluster finding for LHCb silicon pixel VELO detector using FPGA, ACAT 2019, Saas Fee (Switzerland). Link here.

  • 2018 - F. Lazzari et al, Performance of a high-throughput tracking processor implemented on Stratix-V FPGA, Pisa Meeting 2018, https://doi.org/10.1016/j.nima.2018.08.025.

  • 2017 - R. Cenci et al., Development of a High-Throughput Tracking Processor on FPGA Boards, TWEPP 17, https://pos.sissa.it/313/136/ .

  • 2016 - R. Cenci et al. First results of an "artificial retina" processor prototype, MOCAST 2016, https://doi.org/10.1109/MOCAST.2016.7495111.

  • 2016 - R, Cenci et al, First Results of an “Artificial Retina” Processor Prototype, EPJ Web Conf. 127 (2016) 00005.

  • 2016 - MJ. Morello et al, Real-time track reconstruction during readout using an artificial retina architecture, DOI: 10.1109/NSSMIC.2016.8069933.

  • 2016 - R.Cenci et al., An artificial retina processor for track reconstruction at the full LHC crossing rate Nucl. Instrum. Meth. A 824, 260 (2016).

  • 2016 - P .Marino et. al, The artificial retina for track reconstruction at the LHC crossing rate, Nucl.Part.Phys.Proc. 273-275 (2016) 2488-2490.

  • 2015 - N. Neri et al., First results of the silicon telescope using an 'artificial retina' for fast track finding, DOI: 10.1109/ANIMMA.2015.7465644

  • 2015 - D.Tonelli et al., The artificial retina processor for track reconstruction at the LHC crossing rate, JINST 10, 03, C03018 (2015).

  • 2015 P.Marino et al., Simulation and performance of an artificial retina for 40 MHz track reconstruction, JINST 10, 03, C03008 (2015).

  • 2014 - N. Neri et al., First prototype of a silicon tracker using an 'artificial retina' for fast track finding, PoS TIPP 2014, 199 (2014).

  • 2014 - F. Caponio et al, `The readout architecture for the retina-based, Cosmic Ray Telescope, DOI: 10.1109/RTC.2014.7097516

  • 2014 - A. Abba et al., A retina-based cosmic rays telescope, DOI: 10.1109/RTC.2014.7097515.

  • 2014 - G. Punzi et al., A Specialized Processor for Track Reconstruction at the LHC Crossing Rate,JINST 9, C09001 (2014).

Talks at conferences

  • CHEP 2021 - G. Bassi et al., A real-time FPGA-based cluster finding algorithm for LHCb silicon pixel detector, CHEP 2021. Virtual.

  • CTD 2020, F. Lazzari, An optical network for accelerating real-time tracking with FPGAs, Princeton (USA)/Virtual.

  • LHCC 2020, F. Lazzari, Real-time computing solutions for LHCb based on FPGAs, CERN (Geneve).

  • CHEP 2019, G. Tuci, Reconstruction of track candidates at the LHC crossing rate using FPGAs, Adelaide (Australia).

  • VERTEX 2019, G. Punzi, Real-time reconstruction of pixel vertex detectors with FPGAs, Lafodia Sea Resort, Lopud Island (Croatia).

  • IEEE NSS-MIC 2019, G.Bassi, A 2D FPGA-based clustering algorithm for the LHCb silicon pixel detector running at 30 MHz, Manchester (UK).

  • ACAT 2019, M.J.M. Morello, Real-time reconstruction of long-lived particles at LHCb using FPGAs, Saas Fee (Switzerland).

  • ACAT 2019, F. Lazzari, Real-time cluster finding for LHCb silicon pixel VELO detector using FPGA, Saas Fee (Switzerland)

  • Pisa Meeting 2018, F. Lazzari, Performance of a high-throughput tracking processor implemented on Stratix-V FPGA. La Bidola, Isola d'Elba.

  • IFAE 2017, G.Punzi, Detector-embedded tracking using the RETINA algorithm, Trieste.

  • LHCb-Italy 2017, F. Lazzari, Tracking online per la fase 2 di LHCb. Bari.

  • TWEPP 2017, R. Cenci, Development of a High-Throughput Tracking Processor on FPGA Boards, Santa Cruz (USA).

  • SIF 2017, F. Lazzari, A real-time tracking device for LHCb phase-II upgrade, Trento (Ita).

  • Beyond the LHCb Phase-1 Upgrade Workshop (Elba) 2017 , S. Stracka, Real-time reconstruction of dowstream tracks, Elba (Ita).

  • INFN Seminar, R. Cenci, First prototype of a real-time tracker based on "artificial retina" architecture, Pisa.

  • Connecting the dots 2016, R. Cenci, First prototype of an “Artificial Retina” Processor for Track Reconstruction,Vienna.

  • Mocast 2016, S. Stracka, First results of an "artificial retina" processor prototype, Thessaloniki Greece.

  • CHEP 2016 , S. Stracka, An artificial retina processor for track reconstruction at the full LHC crossing rate ,San Francisco (USA).

  • IEEE NSS-MIC 2016, M.J. Morello, Real-time track reconstruction during readout using an artificial retina architecture, Strasbourg (France).

  • XII Seminar on Software for Nuclear, Subnuclear and Applied Physis, G.Punzi, Realtime computing neural systems and future challenges in HEP, An introduction to the RETINA initiative, Alghero 2015.

  • Pisa Meeting 2015, R. Cenci, An “artificial retina” processor for track reconstruction at the full LHC crossing rate. Isola d'Elba (Italy).

  • TIPP 2014, N.Neri, First prototype of a silicon tracker using an artificial retina for fast track finding», Amsterdam .

  • TWEPP 2014, A. Abba, Progress Towards the First Prototype of a Silicon Tracker Using an 'Artificial Retina' for Fast Track Finding», Aix en Provence, France

  • NSS-MIC 2014, N, Neri, First Prototype of a Tracking System with Artificial Retina for Fast Track Finding», Seattle, USA

  • NSS-MIC 2014, A. Abba, Electronic Readout System for Retina-Based Cosmic-Ray Telescope, Seattle, USA.

  • TIP 2014, D. Tonelli, Artificial retina tracking at the LHC crossing rate. Amsterdam (Netherlands).

  • ICHEP 14, The artificial retina for track reconstruction at the LHC crossing rate. Valencia (Spain).

  • WIT 2014, D. Tonelli, The artificial retina processor for track reconstruction at the LHC crossing rate, Philadelphia (USA).

  • WIT 2014, P. Marino, Simulation and performance of an artificial retina for 40 MHz track reconstruction. Philadelphia (USA).

  • INSTR 2014, G. Punzi, A Specialized Processor for Track Reconstruction at the LHC Crossing Rate, Novosibirsky (Russia)

Master theses