High-Level Synthesis (HLS)-Enabled Field-Programmable Gate Array (FPGA) Algorithms for Latency-Critical Plasma Diagnostics and Neural Trigger Prototyping in Next-Generation Energy Projects

Large-scale advanced energy systems, including fusion devices, high-power plasma sources, and accelerator-driven energy platforms, increasingly depend on real-time, hardware-level data processing for diagnostics, control, and protection. In such installations, ultra-low latency, deterministic throughput, and multi-decade operational lifetimes are not optional design goals but strict system-level requirements. While similar timing constraints exist in high-energy physics infrastructures, energy applications place a stronger emphasis on long-term stability, maintainability, and reproducibility of digital signal processing pipelines. This work investigates whether high-level synthesis (HLS) provides a practical and sustainable design methodology for implementing both classical pattern-based and compact neural network (NN) trigger logic on Field-Programmable Gate Arrays (FPGAs) under realistic energy-system constraints. Using representative commercial toolchains (Intel HLS and hls4ml) as reference workflows, we demonstrate the capabilities of fixed-point, fully pipelined streaming architectures, while also identifying critical shortcomings of pragma-driven HLS approaches in terms of architecture transparency, long-term portability, and systematic multi-objective design-space exploration, all of which are crucial for long-lived energy projects and plasma diagnostic systems. These limitations directly motivate the development of a custom, vendor-agnostic, extensible HLS framework (PyHLS), specifically oriented toward deterministic latency, reproducibility, and physics-grade verification demands of advanced energy infrastructures. Gas Electron Multipliers (GEMs) are modern gaseous detectors increasingly employed in plasma diagnostics, radiation monitoring, and high-power energy experiments, where high rate capability, fine spatial resolution, and radiation tolerance are required. Their massively parallel signal structure and continuous data streams make GEMs a representative and demanding benchmark for FPGA-based real-time trigger and preprocessing systems in energy-related environments. The primary objective of this study is to establish a pragmatic technological baseline, demonstrating that contemporary HLS workflows can reliably support both template-based and neural inference-based trigger architectures within strict timing, resource, and power constraints typical for advanced energy installations. Furthermore, we outline a scalable development path toward multi-channel and two-dimensional (pixelated) GEM readout architectures, directly applicable to fusion diagnostics, plasma accelerators, beam–plasma interaction studies, and radiation-hard energy monitoring platforms. Although the proposed methodology remains fully transferable to large-scale physics trigger systems, its principal relevance is directed toward real-time diagnostics and protection layers in next-generation energy systems.