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.
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| Title: | 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. |
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| Authors: | Cieszewski, Radosław1 (AUTHOR) radoslaw.cieszewski@pw.edu.pl, Poźniak, Krzysztof1 (AUTHOR), Romaniuk, Ryszard1 (AUTHOR), Linczuk, Maciej1 (AUTHOR) |
| Source: | Energies (19961073). Feb2026, Vol. 19 Issue 4, p1091. 20p. |
| Subject Terms: | *Field programmable gate arrays, *Plasma diagnostics, *Energy infrastructure, *Real-time computing |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 191973511 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cieszewski%2C+Radosław%22">Cieszewski, Radosław</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> radoslaw.cieszewski@pw.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Poźniak%2C+Krzysztof%22">Poźniak, Krzysztof</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Romaniuk%2C+Ryszard%22">Romaniuk, Ryszard</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Linczuk%2C+Maciej%22">Linczuk, Maciej</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Feb2026, Vol. 19 Issue 4, p1091. 20p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Field+programmable+gate+arrays%22">Field programmable gate arrays</searchLink><br />*<searchLink fieldCode="DE" term="%22Plasma+diagnostics%22">Plasma diagnostics</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+infrastructure%22">Energy infrastructure</searchLink><br />*<searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=191973511 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19041091 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1091 Subjects: – SubjectFull: Field programmable gate arrays Type: general – SubjectFull: Plasma diagnostics Type: general – SubjectFull: Energy infrastructure Type: general – SubjectFull: Real-time computing Type: general Titles: – TitleFull: 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. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cieszewski, Radosław – PersonEntity: Name: NameFull: Poźniak, Krzysztof – PersonEntity: Name: NameFull: Romaniuk, Ryszard – PersonEntity: Name: NameFull: Linczuk, Maciej IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 4 Titles: – TitleFull: Energies (19961073) Type: main |
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