ML4FE Workshop, University of Hawaii

Session

Technology, Contributed

May 19, 2025, 1:10 PM

Conveners

Technology, Contributed

• Yun-Tsung Lai (KEK IPNS)

Technology, Contributed

• Yun-Tsung Lai (KEK IPNS)

Description

Contributed talks, ca. 12' + 3

34. Machine Learning-Assisted Event Classification in Cross-Strip CZT PET Detectors Leveraging Quantum Correlations

Dr Praveen Gurunath Bharathi Gurunath Bharathi, Shiva Abbaszadeh

5/19/25, 1:10 PM

Contributed talk (12'+3')

We present a new approach for positron emission tomography (PET) event classification that integrates machine learning with quantum-aware signal processing. Our system utilizes a cross-strip Cadmium Zinc Telluride (CZT) detector architecture optimized for high-resolution spatial and energy discrimination. By exploiting quantum correlations of annihilation photons, we aim to enhance the...

32. Introduction to eFPGAs and Their Application to High Energy Physics

Larry Ruckman (SLAC National Accelerator Laboratory)

5/19/25, 1:30 PM

Contributed talk (12'+3')

Advancements in High Energy Physics (HEP) increasingly rely on intelligent instrumentation capable of processing vast, complex datasets in real time. As detectors evolve, front-end electronics must not only manage extreme data rates with minimal latency and power consumption but also withstand harsh environmental conditions, such as high radiation and cryogenics. Traditional...

44. Reconfigurable Pulse-Shape Discrimination Algorithm Implementations using eFPGAs

Carl Grace (Lawrence Berkeley National Laboratory)

5/19/25, 1:50 PM

Contributed talk (12'+3')

We present our ongoing work toward developing machine learning (ML) algorithms for embedded Field-Programmable Gate Arrays (eFPGAs) integrated on readout Application-Specific Integrated Circuits (ASICs). Our focus is on reconfigurable Pulse-Shape Discrimination (PSD), a critical signal processing technique for neutron imaging and other imaging modalities. By leveraging the reconfigurability of...

36. Empowering AI Implementation: The Versatile SLAC Neural Network Library (SNL) for FPGA

Abhilasha Dave (SLAC National Accelerator Laboratory)

5/19/25, 2:10 PM

Contributed talk (12'+3')

The SLAC Neural Network Library (SNL) is a high-performance, hardware-aware framework for deploying machine learning models on FPGAs at the edge of the scientific data chain. Developed using Xilinx's High-Level Synthesis (HLS) tools, SNL combines the flexibility of software-defined design with the low-latency, high-throughput advantages of reconfigurable hardware. It offers a user-friendly API...

59. Modern Machine Learning Model Deployment on FPGA for KamLAND-Zen

Zepeng Li

5/19/25, 2:30 PM

Contributed talk (12'+3')

cgra4ml is a highly flexible, high-performance accelerator system that helps researchers build, train, and implement machine learning models on Field Programmable Gate Arrays (FPGAs). It extends the capabilities of HLS4ML by allowing off-chip data storage and supporting a broader range of neural network architectures, including models like ResNet and PointNet. Using this new framework, we...