Theory Meets Practice #3: Embedded AI - Deep Learning Models on 5€ microcontrollers

We are excited to host a BLISS x dida workshop featuring Elias Trommer from Infineon, who will guide us through an interactive session on Embedded AI.
Title: Deep Learning Models on 5€ microcontrollers
📅 Date: 29.04.2026
🕕 Time: 18:00
📍 Location: TU Berlin Marchstrasse 23 [Room 0.011]
The session will last around 2 hours, followed by a networking session with dida and fellow AI enthusiasts (and free pizza!🍕). Bring your laptop!

Abstract: In this workshop, we will discuss the unique challenges (and solutions) faced when deploying Deep Learning models to embedded microcontrollers. These tiny devices are increasingly capable of running models, even for relatively complex use cases, but come without many of the comforts of a classic computer. When deploying a model to a microcontroller, a much deeper understanding of the physical hardware and good knowledge of the various optimization paths that help reduce the resource consumption of an application is needed. We aim to shed some light on how to deploy models to these cheap, ubiquitous and sometimes surprisingly capable little pieces of hardware.
In this session, we will cover:
Important differences between a desktop/server and a bare-metal embedded system
Quantization for model compression
Anatomy of an Embedded Application
Embedded ML Inference Engines and how to run them
Deploying a model step by step
Who is this event for?
This session is targeted at anyone with basic ML knowledge. Experience with C and embedded systems is probably helpful, but not required. We will cover the most important aspects as part of the workshop.

We are BLISS e.V., Berlin’s AI community connecting like-minded individuals passionate about machine learning and data science. Our BLISS Workshops connect students and young professionals with industry partners, offering an inside look into how machine learning is applied in real-world settings - from research and development to deployment.
We are dida, scientific engineers who believe that reliable AI should not be a "black box". That is why we prioritize transparency, mathematics, and code over hype. By bridging the gap between theoretical research and production, we develop custom white-box AI solutions that are fully explainable, rigorously engineered, and free of "magic".
dida Website: https://dida.do
dida Youtube: https://www.youtube.com/@dida-do
BLISS Website: https://bliss.berlin
BLISS Youtube: https://www.youtube.com/@bliss.ev.berlin