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A packet-level digital hardware twin for commissioning megahertz diagnostic edge AI and plasma control system integration in tokamaks
Semin Joung, Abhilasha Dave, Luca Scomparin +6
High-bandwidth plasma diagnostics increasingly provide inputs to machine learning and signal-processing algorithms intended for real-time tokamak control, but the complete path fro…
Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments
Ivan Ge, Sagar Addepalli, Abhilasha Dave +1
Quantum machine learning (QML) algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in high-dimensional collider data,…
Transformer-based machine learning using low-level calorimeter signals for collimated photon identification at collider experiments
Gabriel Matos, Lauren Larson, Abhilasha Dave +8
Electromagnetic calorimeters provide essential information for reconstructing and selecting both Standard Model (SM) and potential beyond the SM physics events at high-energy parti…
HeteroViT: A Versatile Single-Layer Vision Transformer Concept, Co-Designed for Distributed Real-Time Data Reduction on Scientific Detectors
Abhilasha Dave, Weijian Zheng, Antonino Miceli +3
Next-generation X-ray detectors generate data faster than any system can affordably store or process. LCLS-II, the upgraded Linac Coherent Light Source at SLAC, produces data on th…
Discrete Wavelet Transform for Serial X-ray Crystallography Image Segmentation
Dionisio Doering, Noemie Claret, Guilherme Paulino +10
Upcoming LCLS-II/II-HE operation at repetition rates approaching 1MHz demands on-detector data reduction to manage the resulting data volumes. We present a 2D discrete wavelet tran…
FPGA-Accelerated Real-Time Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference
Abhilasha Dave, Semin Joung, SangKyeun Kim +11
In this work, we demonstrate the deployment of a hardware-accelerated machine learning (ML) inference system integrated into a real-time processing at the DIII-D tokamak fusion rea…
Hardware-Aware Tensor Networks for Real-Time Quantum-Inspired Anomaly Detection at Particle Colliders
Sagar Addepalli, Prajita Bhattarai, Abhilasha Dave +1
Quantum machine learning offers the ability to capture complex correlations in high-dimensional feature spaces, crucial for the challenge of detecting beyond the Standard Model phy…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…