ML model optimization pipeline development

About

**Project Description**: Machine learning is rapidly replacing heuristic-based algorithms across many domains, and ATLAS plans to make extensive use of ML in both its hardware and software triggers during the HL-LHC era. While mature optimization platforms exist for deploying ML in software, very few MLOps pipelines targeting custom FPGA-based electronics have been tested at scale. Within Next-Generation Triggers (NGT) WP2.1, we are developing a dedicated MLOps pipeline that automatically optimizes and deploys ML algorithms for the ATLAS L0-Global hardware trigger. A prototype version already exists and is in use, while a more generalized approach, one that can potentially extend beyond the specific scope of WP2.1, is being developed in parallel.

This work covers three areas: generating open data samples for validating pipeline developments; developing a command-line interface that allows users to create standalone ML algorithms, integrate them into the pipeline, and deploy custom pipeline stages; and integrating a software stack dedicated to the joint optimization of multiple trigger algorithms. The goal is a service-level design through which ML developers for hardware triggers can easily obtain deployable, optimized trigger algorithms that efficiently utilize the underlying hardware. Such a service significantly lowers the barrier to integrating ML into the ATLAS hardware trigger and ensures that common, well-defined metrics are used to evaluate whether deploying an ML model there provides a real advantage.

**Supervisor**: Ioannis Xiotidis, Noah Clarke Hall

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