Speaker
Description
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 eFPGAs, this approach enables dynamic optimization of the PSD algorithm for specific particle energies and scintillator combinations. Hardware testing of a standalone eFPGA is currently underway, and we are exploring lightweight ML models and feature extraction techniques compatible with the power and area constraints of on-chip inference. This work lays the foundation for flexible, low-power PSD implementations in next-generation radiation detection systems.
