Testbeam results of the first real-time embedded tracking system with artificial retina

Neri, N.; Abba, A.; Caponio, F.; Citterio, M.; Coelli, S.; Fu, J.; Merli, A.; Monti, M.; Petruzzo, M.

doi:10.1016/j.nima.2016.05.129

Cited by 4 publications

(6 citation statements)

References 5 publications

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“…And the third block calculates the track parameters. The authors present the testbeam results in Neri et al (2017).…”

Section: Sub-atomic Particle Trackingmentioning

confidence: 99%

“…After 2006, there is steady increase in the research on the subject, indicating that the potential of the technology has been discovered. (Etienne-Cummings and Van der Spiegel, 1996;Delbruck and Liu, 2012;Posch et al, 2014;Gallego et al, 2020), Period of 2002(Delbruck, 2016, neuromorphic chips , VLSI neuromorphic circuits (Indiveri, 2008), spiking neural networks (Brette et al, 2007) Benchmarks Guidelines for benchmark creation (Tan et al, 2015), dataset (Gibson et al, 2014a;Li et al, 2017), object recognition (Serrano-Gotarredona and Linares-Barranco, 2015), action recognition and tracking (Hu et al, 2016), 3D perception (Zhu et al, 2018), driving applications (Binas et al, 2017) Applications Tracking Object tracking (Gómez-Rodríguez et al, 2011;Saner et al, 2014;Delbruck et al, 2015;Zong et al, 2018), multiple object (Gómez-Rodríguez et al, 2010;Linares-Barranco et al, 2015), camera movement (Kim et al, 2008;Reinbacher et al, 2017), feature tracking (Lagorce et al, 2015b;Ni et al, 2015;Alzugaray and Chli, 2018a), stereo tracking (Schraml et al, 2010b;Müller and Conradt, 2012), camera pose (Gallego et al, 2015(Gallego et al, , 2016(Gallego et al, , 2018aMueggler et al, 2015c), micro-particle tracking (Drazen et al, 2011;Borer et al, 2017), subatomic particle tracking (Neri et al, 2015(Neri et al, , 2017, car tracking (Litzenberger et...…”

Section: Summary Of Reviewed Researchmentioning

confidence: 99%

See 1 more Smart Citation

Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review

Tayarani-Najaran

Schmuker

2021

Front. Neural Circuits

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The nervous systems converts the physical quantities sensed by its primary receptors into trains of events that are then processed in the brain. The unmatched efficiency in information processing has long inspired engineers to seek brain-like approaches to sensing and signal processing. The key principle pursued in neuromorphic sensing is to shed the traditional approach of periodic sampling in favor of an event-driven scheme that mimicks sampling as it occurs in the nervous system, where events are preferably emitted upon the change of the sensed stimulus. In this paper we highlight the advantages and challenges of event-based sensing and signal processing in the visual, auditory and olfactory domains. We also provide a survey of the literature covering neuromorphic sensing and signal processing in all three modalities. Our aim is to facilitate research in event-based sensing and signal processing by providing a comprehensive overview of the research performed previously as well as highlighting conceptual advantages, current progress and future challenges in the field.

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“…And the third block calculates the track parameters. The authors present the testbeam results in Neri et al (2017).…”

Section: Sub-atomic Particle Trackingmentioning

confidence: 99%

Section: Summary Of Reviewed Researchmentioning

confidence: 99%

Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review

Tayarani-Najaran

Schmuker

2021

Front. Neural Circuits

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“…A real-time tracking prototype based on artificial retina algorithm has been recently tested on beam at the CERN SPS with a track rate of about 300 kHz, demonstrating the feasibility of the approach [10]. A device capable of real-time track reconstruction, providing track information with a latency of few microseconds, would improve trigger decisions for the selection of signal events and would reduce the amount of data to transfer, saving bandwidth and resources for data storage.…”

Section: Real-time 4d Trackingmentioning

confidence: 99%

4D fast tracking for experiments at the High Luminosity LHC

Fiorini¹,

Cardini²,

Luppi³

et al. 2017

Proceedings of the 25th International Workshop on Vertex Detectors — PoS(Vertex 2016)

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We propose an innovative detector capable of performing high resolution tracking with ultraprecise time tagging to allow the High Luminosity LHC (HL-LHC) experiments to fully profit from the luminosity upgrade of the accelerator. The increase in the number of visible interactions per bunch crossing will lead to a much larger number of primary vertices and tracks compared to the current LHC conditions, and will inevitably increase tracking inefficiency and ghost track rate. We aim to develop a detector capable of providing 4-dimensional (4D) information, with time resolution in the order of 10 ps and position resolution of about 10 µm, able to operate in a harsh radiation environment exceeding 10 16 1-MeV neutrons equivalent per cm 2 and with fast online track triggering capabilities. The precise measurement of the hits time is the key feature to operate an effective pattern recognition that guarantees a high tracking efficiency while enhancing ghost track rejection, and to perform selective track triggering. This detector system will allow to perform precision physics measurements at the HL-LHC operating at instantaneous luminosities almost one order of magnitude larger than the current ones, making efficient use of the whole delivered luminosity.

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“…Ultimately, the retina algorithm provides an analog response for the track matching contrarily to the AM that returns a "yes/no" pattern matching response. A real-time tracking prototype based on artificial retina algorithm has been recently tested on beam at the CERN SPS with a track rate of about 300 kHz, demonstrating the feasibility of the approach [7]. Studies are ongoing to evaluate the performance at high event rates, e.g.…”

Section: Conceptual Design For a 4d Fast Tracking Pixel Detectormentioning

confidence: 99%

“…R&D for track trigger based on "ad-hoc" processors for fast track reconstruction are in progress for the HL-LHC experiments. We consider a solution based on commercial FPGA [6,7] aiming at reconstructing tracks in real time at 40 MHz input rate. This would allow efficient selection of signal events, reduction of computing resources and data to transfer for subsequent analysis.…”

Section: Introductionmentioning

confidence: 99%

4D fast tracking for experiments at high luminosity LHC

et al. 2016

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A: The full exploitation of the physics potential of the high luminosity LHC is a big challenge that requires new instrumentation and innovative solutions. We present here a conceptual design and simulation studies of a fast timing pixel detector with embedded real-time tracking capabilities. The system is conceived to operate at 40 MHz event rate and to reconstruct tracks in real-time, using precise space and time 4D information of the hit, for fast trigger decisions. This work is part of an R&D project aimed at building an innovative tracking detector with superior time (10 ps) and position (10 µm) resolutions to be used in very harsh radiation environments, for the ultimate flavour physics experiment at the high luminosity phase of the LHC.

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Testbeam results of the first real-time embedded tracking system with artificial retina

Cited by 4 publications

References 5 publications

Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review

Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review

4D fast tracking for experiments at the High Luminosity LHC

4D fast tracking for experiments at high luminosity LHC

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