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25th IEEE Real Time Conference - La Biodola, Elba, Italy

25–29 May 2026
La Biodola - Isola d'Elba (Italy)
Europe/Rome timezone
Deadline for submission of Conference Records and TNS manuscripts extended to July 6, 2026.

General Inquiries

Session

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

29 May 2026, 10:30
Maria Luisa Room (Hotel Hermitage)

Maria Luisa Room

Hotel Hermitage

Conveners

AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing

  • Audrey Corbeil Therrien (Université de Sherbrooke)

Presentation materials

There are no materials yet.

  1. 83. Rapid Neutron Source Identification with Scatter-Based Spectrometers
    Dr David Breitenmoser (University Of Michigan)
    29/05/2026, 10:30
    AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing
    Oral presentation

    Reliable neutron source identification is essential for nuclear nonproliferation, safeguards, and homeland security, yet remains challenging due to the ill-conditioned nature of neutron spectral inversion. Here, we present a scalable Bayesian framework that overcomes these limitations through evidence-based model selection. We introduce a Bayesian Evidence Adaptive Pursuit (ABEP) algorithm...

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  2. 109. Real-Time Toroidal Equilibrium Reconstruction for RFX-mod2 via Quantized Neural Network in FPGA
    Lorenzo Saccaro (Università di Padova-Centro Ricerche Fusione, Italy; Consorzio RFX)
    29/05/2026, 10:50
    AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing
    Oral presentation

    In preparation for the forthcoming operation of RFX-mod2 in reversed-field pinch configuration, a major upgrade to the experiment’s real-time control system is the transition from a simplified cylindrical approximation to the more accurate toroidal geometry.
    However, the existing toroidal reconstruction code is only currently available for post-shot analysis, as it does not satisfy the timing...

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  3. 195. Towards FPGA–Memristive In-Memory Computing for Real-Time Inference in Artificial Intelligence
    Dr Raffaele Giordano (Universita di Napoli Federico II (IT))
    29/05/2026, 11:10
    AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing
    Oral presentation

    As trigger and data acquisition system complexity grows, processing near the detectors becomes essential to cope with throughput and event selection requirements. In this context, real-time artificial intelligence (AI) is gaining momentum. While FPGAs with AI engines are available, they are limited by power consumption, area, and latency due to the von Neumann bottleneck. In-memory computing...

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  4. 221. Co-Design for Ultra-Small ML on FPGAs with an Open-Source Toolchain and AI Agentic Workflow
    Qibin Liu (SLAC National Accelerator Laboratory)
    29/05/2026, 11:30
    AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing
    Oral presentation

    Modern advances in machine learning and microelectronics enable efficient real-time, on-chip data processing under strict latency, power, and bandwidth constraints. Compact models implemented directly in hardware can replace fixed logic to perform intelligent feature extraction, classification, or denoising at the detector front-end, supporting applications from detector readout in high energy...

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  5. 187. A Real Time Archiving Framework from EPICS to Time Series Databases for Fusion Plant Data
    Dr Guang Yang (University of Science and Technology of China)
    29/05/2026, 11:50
    Data Acquisition and Trigger Architectures
    Oral presentation

    Superconducting tokamak fusion facilities continuously generate large volumes of plant data that must be archived reliably and queried efficiently to support system monitoring, fault analysis, and long-term operation. Currently, fusion devices widely adopt the EPICS (Experimental Physics and Industrial Control System) architecture, and plant data are typically archived in files or relational...

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  6. 72. Deep-learning based Real-time Optical Plasma Boundary Detection for Plasma Shape Control on EAST Tokamak
    Qirui Zhang (Institute Of Plasma Physics, Chinese Academy Of Sciences)
    AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing
    Oral presentation

    Real-time and accurate plasma boundary reconstruction is critical for tokamak plasma control. Visible light diagnostics offer a promising solution for plasma shape control during steady-state discharges. In this study, a two-stage plasma boundary detection framework (YOLO-GRAY) was developed on EAST tokamak. The framework utilizes YOLO to rapidly localize the optical emission region, followed...

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  7. 113. Trigger-level track reconstruction and identification with machine learning and FPGAs for ATLAS
    Punit Sharma (Brookhaven National Laboratory (US))
    AI, Machine Learning, Real Time Simulation, Intelligent Signal Processing
    Oral presentation

    At high-luminosity hadron colliders, trigger systems play a key role in preserving sensitivity to rare signals of new physics, even as they filter out the overwhelming background. This becomes especially challenging at the upcoming High-Luminosity Large Hadron Collider (HL-LHC), where extremely high event rates and pileup conditions are expected at the ATLAS experiment. We explore the...

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