[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81845-en":3,"doc-seo-81845-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},81845,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","APEIRON: Composing Smart TDAQ Systems for High Energy Physics Experiments","APEIRON is a distributed heterogeneous processing framework combining hardware architecture and a complete software stack for multi-FPGA systems. Built for smart trigger and data acquisition (TDAQ) in high energy physics, it covers the whole hierarchy from low-level device drivers to a high-level dataflow programming model grounded in High-Level Synthesis. The work details the framework design, its communication infrastructure, and a particle identification application for the NA62 experiment as a representative use case.","arXiv :2607 .02429v1 [physics .ins-det] 2 Jul 2026  \nAPEIRON: composing smart TDAQ systems for high energy physics experiments  \nRoberto Ammendola2 , Andrea Biagioni 1 , Carlotta Chiarini3 , 1 , Andrea Ciardiello3 , 1 , Paolo Cretaro 1 , Ottorino Frezza 1 , Francesca Lo Cicero 1 , Alessandro Lonardo 1 , Michele Martinelli 1 , Pier Stanislao Paolucci 1 , Pierpaolo Perticaroli 1 , Cristian Rossi 1 , Francesco Simula 1 , Matteo Turisini 1 , Piero Vicini 1  \n1 Istituto Nazionale di Fisica Nucleare (INFN), sezione di Roma, Rome, Italy  \n2 Istituto Nazionale di Fisica Nucleare (INFN), sezione di Roma Tor Vergata, Rome, Italy  \n3 Dipartimento di Fisica, Sapienza Universit`a di Roma, Rome, Italy E-mail: [alessandro.lonardo@roma1.infn.it](alessandro.lonardo@roma1.infn.it)  \nAbstract.  \nWe present APEIRON, a distributed heterogeneous processing framework comprising both hardware architecture and software stack for multi-FPGA systems. Targeting smart trigger and data acquisition (TDAQ) systems in high energy physics, APEIRON spans the full software hierarchy: from low-level device drivers to a high-level dataflow programming model based on High-Level Synthesis. We describe the framework design, its core communication infrastructure, and a particle identification application for the NA62 experiment as a representative physics use case.  \n1. Motivation and design goals  \nReal-time dataflow processing in experimental particle physics places stringent demandson computing throughput, deterministic latency and I/O bandwidth. FPGA devices are particularly well suited to these requirements owing to their reconfigurable logic, tightly coupled memory and high-speed serial transceivers. The maturation of High-Level Synthesis (HLS) tools over the past decade has substantially lowered the entry barrier, allowing a wider community of physicists and engineers to exploit FPGA acceleration without resorting exclusively to Hardware Description Language workflows.  \nA significant limitation of present-day HLS environments, however, is their confinement toa single FPGA device. When the scale of a trigger or data-acquisition problem exceeds the capacity of one chip—as is common in modern experiments with high channel counts and event rates—developers must fall back on ad-hoc, manually crafted inter-FPGA communication layers. This gap motivated the creation of APEIRON: an integrated framework that extends the Xilinx Vitis HLS ecosystem to operate transparently across a network of interconnected FPGAs.  \nThe principal design goals can be summarised as follows:  \n• provide a modular, topology-configurable, low-latency direct interconnect among FPGA nodes;  \n• offer a dataflow programming abstraction, drawing on Kahn Process Networks [1], in which processing tasks communicate through lightweight send/receive primitives regardless of their physical placement;  \n• automate the generation of all ancillary logic (routing, dispatching, aggregation) from a compact application description, so that users concentrate on the algorithmic C/C++ kernels;  \n• target both traditional low-level trigger systems and data-reduction stages in trigger-less or streaming readout experimental setups characterised by high event rates.  \n2. Platform architecture  \n2.1. Overall topology  \nAt the system level, APEIRON models the data path of a trigger or data-reduction chain as m independent data sources (detectors or sub-detectors) feeding a cascade of n stream-processing stages. Each stage may reside on one or more FPGA nodes; the network fabric recombines data streams across stages as required by the physics algorithm. Figure 1 illustrates a representative configuration.  \nFigure 1 . Data-stream recombination across specialised processing stages in a trigger or datareduction pipeline built with APEIRON.  \nThe scalability of this scheme rests on the network infrastructure: a packet-switched, dimension-order-routed mesh whose physical topology can be tailored to the application through co","cbCaiptL9prlbFyV","https://ap.wps.com/l/cbCaiptL9prlbFyV","pdf",1075423,5,1,6,"English","en",105,"# Motivation and design goals\n# Platform architecture\n## Overall topology\n## Communication infrastructure\n## Kernel interface and programming model","[{\"question\":\"What problem does APEIRON address in high energy physics TDAQ systems?\",\"answer\":\"It targets the limitation of current High-Level Synthesis environments that are confined to a single FPGA, by providing a framework that works transparently across a network of interconnected FPGAs for real-time trigger and data acquisition workloads.\"},{\"question\":\"How does APEIRON support scalable multi-FPGA data processing?\",\"answer\":\"It models a processing pipeline as multiple independent data sources feeding multiple stream-processing stages, with packet-switched network fabric that recombines streams across stages according to the physics algorithm.\"},{\"question\":\"What communication and routing mechanisms are used in APEIRON?\",\"answer\":\"The communication subsystem uses an adapted network-on-chip approach with low-latency scalable bandwidth, supporting intra-node and inter-node packet transfers, and employing dimension-order routing with virtual channels and Virtual Cut-Through switching.\"}]","APEIRON: Composing Smart TDAQ Systems for High Energy Physics Experiments | 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problem does APEIRON address in high energy physics TDAQ systems?","Question",{"text":77,"@type":78},"It targets the limitation of current High-Level Synthesis environments that are confined to a single FPGA, by providing a framework that works transparently across a network of interconnected FPGAs for real-time trigger and data acquisition workloads.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does APEIRON support scalable multi-FPGA data processing?",{"text":82,"@type":78},"It models a processing pipeline as multiple independent data sources feeding multiple stream-processing stages, with packet-switched network fabric that recombines streams across stages according to the physics algorithm.",{"name":84,"@type":75,"acceptedAnswer":85},"What communication and routing mechanisms are used in APEIRON?",{"text":86,"@type":78},"The communication subsystem uses an adapted network-on-chip approach with low-latency scalable bandwidth, supporting intra-node and inter-node packet 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