Speaker
Description
Machine-learning algorithms are becoming central to real-time event selection at the LHC, where future trigger systems must process substantially more complex detector information at fixed, sub-microsecond latencies. These constraints create a growing need for flexible workflows that can map large neural networks onto heterogeneous trigger hardware while preserving strict timing budgets. We present recent developments in hls4ml aimed at supporting these physics-driven requirements for future trigger systems, achieved as part of the ongoing Next-Generation Triggers (NGT) project.
The new Multi-Graph feature enables large neural networks to be decomposed into multiple subgraphs at chosen layer boundaries, allowing experiments to explore modular deployment strategies such as distributing subgraphs across FPGA regions, balancing latency paths, and performing step-wise optimisation on models that exceed standard HLS tool limits. As an additional benefit, the subgraphs created by the Multi-Graph feature can be synthesized in parallel, resulting in a great reduction in synthesis time (up to 3.5×) and enhanced debugging flexibility.
Complementing this trigger-motivated modularisation workflow, we introduce a plugin-based backend system that allows support for additional hardware targets to be developed externally. As an example, we highlight the aie4ml plugin, which brings support for AMD AI Engines and demonstrates how hls4ml’s parsing and quantisation infrastructure can be reused for non-HLS toolflows. While this work is independent of the Multi-Graph development, the plugin system provides the foundation for future studies in which subgraphs may be targeted to different accelerator technologies. In this way, these features illustrate how hls4ml is evolving into an extensible ecosystem capable of accommodating heterogeneous hardware relevant to long-term trigger R&D. These developments position hls4ml to better support the modular and scalable algorithm-design workflows required for trigger systems at the HL-LHC and beyond.