Hardware-Aware AutoML for Edge AI
Organizers: Andreas Kist, Sebastian Zimmermann, René Groh
Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Correspondence: andreas.kist@fau.de
Edge AI brings machine learning closer to sensors, users, and real-world decision points, but deploying models on microcontrollers and other highly constrained devices remains challenging. Accuracy alone is rarely sufficient: models must also satisfy strict constraints on latency, memory footprint, energy consumption, robustness, and deployability. This makes Edge AI a natural and timely application area for Automated Machine Learning.
This tutorial will provide an accessible introduction to hardware-aware AutoML for Edge AI. We will first motivate why edge deployment is increasingly relevant for the AutoML community and explain the specific constraints that distinguish microcontroller-class hardware from cloud or GPU-based deployment. We will then introduce common strategies for bringing models to embedded devices, including architecture design, quantization, pruning, compilation, benchmarking, and hardware-in-the-loop evaluation. The second half of the tutorial will be hands-on: participants will use MIMaaS, a Microcontroller-as-a-Service platform, to evaluate candidate models on physical hardware and interpret latency, memory, and power measurements in a multi-objective AutoML setting. The tutorial will close with open research challenges and discussion.
From AutoML to AgenticML: LLMs as the New SearchOperator
Organizers: Blaž Škrlj (Teads, Jožef Stefan Institute), Sebastian Koralewski (Teads)
Correspondence: blaz.skrlj@teads.com, sebastian.koralewski@teads.com
AutoML treats model design as a search over hyperparameters, architectures, and pipelines, a processusually driven by Bayesian optimization, evolutionary algorithms, or bandits over fixed encodings. Arecent line of work replaces that operator with a large language model. By processing naturallanguage task and past performance scores, the LLM directly proposes new candidates, mutatespopulations, or rewrites programs. This tutorial is a field overview of that idea. Part I: Introductionto LLM-Driven Search surveys the main patterns and the methods that instantiate them:hyperparameter tuning and Bayesian optimization (LLAMBO), neural architecture search (GENIUS),prompt optimization (OPRO, EvoPrompt), program and algorithm discovery (FunSearch,AlphaEvolve), reward design (Eureka), and feature engineering (CAAFE, PromptFE), relating each toevolutionary search and learned optimizers (OptFormer). It then examines what the publishedevidence does and does not establish: compute-matched comparison, evaluation noise, andbenchmark contamination (MLE-bench). Part II: A Case Study from Practice grounds the survey in avendor-neutral case study detailing a static pipeline turned into an LLM-driven loop. It examines howthe model proposes configurations, evaluates results, and iterates, alongside a discussion ofguardrails, cost controls, and failed attempts.