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

Session

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

29 May 2026, 10:30

Maria Luisa Room (Hotel Hermitage)

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)

29/05/2026, 10:30

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...

83. Rapid Neutron Source Identification with Scatter-Based Spectrometers

Dr David Breitenmoser (University Of Michigan)

29/05/2026, 10:50

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...

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, 11:10

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...

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, 11:30

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...

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, 11:50

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...

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...