Hardware Implementation Study of Particle Tracking Algorithm on FPGAs

Gabrielli, Alessandro; Alfonsi, F.; Annovi, A.; Camplani, A.; Cerri, A.

doi:10.3390/electronics10202546

Cited by 4 publications

(7 citation statements)

References 9 publications

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“…Recently, the HT algorithm has been studied and implemented on many electronic cards [8][9][10][11]. In particular, in this study we show the algorithm investigated for tracking detectors in HEPE, where it is used to extract trajectories and straight or curved lines, within digital image representations of the particle space.…”

Section: Pattern Recognition In Hepe Using the Hough Transformmentioning

confidence: 99%

“…Formula (1) executes the conversion from the particle space to the HS using (r) as the radius of the detector layer in the real transversal plane, (ϕ) as the angle of the Hit with respect to the horizontal axis; let us say that these are the input parameters. On the contrary, (φ 0 ) is the same angle of the particle trajectory with respect the origin, and let us call it an output parameter of Formula (1), along with the ( Aq Pt ), which is described below (see [8,9] for details). Formula (1) computes, for each pair of Hits, many operations by varying (φ 0 ) angles and, consequently, any Hit in the parameter space creates many straight-lines in the HS.…”

Section: Aqmentioning

confidence: 99%

“…If more Roads are generated, these will be eliminated by a further cross-check using additional layers than those used in the HT process. In [8,9], we report a hardware implementation, with the detail of the block diagram, while in this paper we broaden the field to generic low latency applications, and this is why we have designed a software development tool. Compared to other similar devices [19][20][21], FPGA components can simultaneously provide low and fixed latency, low power budget, and high data rate.…”

Section: Simulationsmentioning

confidence: 99%

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Hough Transform Proposal and Simulations for Particle Track Recognition for LHC Phase-II Upgrade

Gabrielli

Alfonsi

Corso

2022

Sensors

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In the near future, LHC experiments will continue future upgrades by overcoming the technological obsolescence of the detectors and the readout capabilities. Therefore, after the conclusion of a data collection period, CERN will have to face a long shutdown to improve overall performance, by updating the experiments, and implementing more advanced technologies and infrastructures. In particular, the largest LHC experiment, i.e., ATLAS, will upgrade parts of the detector, the trigger, and the data acquisition system. In addition, the ATLAS experiment will complete the implementation of new strategies, algorithms for data handling, and transmission to the final storage apparatus. This paper presents an overview of an upgrade planned for the second half of this decade for the ATLAS experiment. In particular, we show a study of a novel pattern recognition algorithm used in the trigger system, which is a device designed to provide the information needed to select physical events from unnecessary background data. The idea is to use a well known mathematical transform, the Hough transform, as the algorithm for the detection of particle trajectories. The effectiveness of the algorithm has already been validated in the past, regardless of particle physics applications, to recognize generic shapes within images. On the contrary, here, we first propose a software emulation tool, and a subsequent hardware implementation of the Hough transform, for particle physics applications. Until now, the Hough transform has never been implemented on electronics in particle physics experiments, and since a hardware implementation would provide benefits in terms of overall Latency, we complete the studies by comparing the simulated data with a physical system implemented on a Xilinx hardware accelerator (FELIX-II card). In more detail, we have implemented a low-abstraction RTL design of the Hough transform on Xilinx UltraScale+ FPGAs as target devices for filtering applications.

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Section: Pattern Recognition In Hepe Using the Hough Transformmentioning

confidence: 99%

Section: Aqmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

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Hough Transform Proposal and Simulations for Particle Track Recognition for LHC Phase-II Upgrade

Gabrielli

Alfonsi

Corso

2022

Sensors

Self Cite

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“…We have shown the possibility of implementing the Hough transform (HT) in pattern recognition problems within "high-energy" physics experiments where "less-performing" solutions would have been abandoned. In [9], we report in detail an HW implementation (with the block diagram), while, in this paper, we broaden the field to more generic low-latency applications by using the design of an SW development tool. In [9], Xilinx UltraScale+ FPGA is investigated.…”

Section: Introductionmentioning

confidence: 99%

“…In [9], we report in detail an HW implementation (with the block diagram), while, in this paper, we broaden the field to more generic low-latency applications by using the design of an SW development tool. In [9], Xilinx UltraScale+ FPGA is investigated. The device features many gates, high-bandwidth memories, transceivers, and other high-performance electronics in a single chip, enabling the design of large, complex, and scalable architectures.…”

Section: Introductionmentioning

confidence: 99%

Simulated Hough Transform Model Optimized for Straight-Line Recognition Using Frontier FPGA Devices

2022

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The use of the Hough transforms to identify shapes or images has been extensively studied in the past using software for artificial intelligence applications. In this article, we present a generalization of the goal of shape recognition using the Hough transform, applied to a broader range of real problems. A software simulator was developed to generate input patterns (straight-lines) and test the ability of a generic low-latency system to identify these lines: first in a clean environment with no other inputs and then looking for the same lines as ambient background noise increases. In particular, the paper presents a study to optimize the implementation of the Hough transform algorithm in programmable digital devices, such as FPGAs. We investigated the ability of the Hough transform to discriminate straight-lines within a vast bundle of random lines, emulating a noisy environment. In more detail, the study follows an extensive investigation we recently conducted to recognize tracks of ionizing particles in high-energy physics. In this field, the lines can represent the trajectories of particles that must be immediately recognized as they are created in a particle detector. The main advantage of using FPGAs over any other component is their speed and low latency to investigate pattern recognition problems in a noisy environment. In fact, FPGAs guarantee a latency that increases linearly with the incoming data, while other solutions increase latency times more quickly. Furthermore, HT inherently adapts to incomplete input data sets, especially if resolutions are limited. Hence, an FPGA system that implements HT is inefficient for small sets of input data but becomes more cost-effective as the size of the input data increases. The document first presents an example that uses a large Accumulator consisting of 1100 × 600 Bins and several sets of input data to validate the Hough transform algorithm as random noise increases to 80% of input data. Then, a more specifically dedicated input set was chosen to emulate a real situation where a Xilinx UltraScale+ was to be used as the final target device. Thus, we have reduced the Accumulator to 280 × 280 Bins using a clock signal at 250 MHz and a few tens input points. Under these conditions, the behavior of the firmware matched the software simulations, confirming the feasibility of the HT implementation on FPGA.

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A low-latency graph computer to identify metastable particles at the Large Hadron Collider for real-time analysis of potential dark matter signatures

Kotwal,

Kemeny,

Yang

et al. 2024

Sci Rep

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Image recognition is a pervasive task in many information-processing environments. We present a solution to a difficult pattern recognition problem that lies at the heart of experimental particle physics. Future experiments with very high-intensity beams will produce a spray of thousands of particles in each beam-target or beam-beam collision. Recognizing the trajectories of these particles as they traverse layers of electronic sensors is a massive image recognition task that has never been accomplished in real time. We present a real-time processing solution that is implemented in a commercial field-programmable gate array using high-level synthesis. It is an unsupervised learning algorithm that uses techniques of graph computing. A prime application is the low-latency analysis of dark-matter signatures involving metastable charged particles that manifest as disappearing tracks.

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Hardware Implementation Study of Particle Tracking Algorithm on FPGAs

Cited by 4 publications

References 9 publications

Hough Transform Proposal and Simulations for Particle Track Recognition for LHC Phase-II Upgrade

Hough Transform Proposal and Simulations for Particle Track Recognition for LHC Phase-II Upgrade

Simulated Hough Transform Model Optimized for Straight-Line Recognition Using Frontier FPGA Devices

A low-latency graph computer to identify metastable particles at the Large Hadron Collider for real-time analysis of potential dark matter signatures

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