Modern machine learning (ML) architectures consume unprecedented amounts of energy, for a single training session often exceeding the energy and carbon footprint of a car during its entire lifetime. If the current growth rate continues ML models may outrun the traffic sector in the global energy balance in 10-20 years. Biological brains, by contrast, are extremely energy efficient. The Sustainable Machine Learning group identifies the mechanisms that enable the striking energy-efficiency of biological brains and explores new approaches to significantly reduce the energy footprint of machine learning using hybrid ML/bio-inspired models.

Our mission

Our mission is to develop state-of-the-art machine learning models that scale to real-world problems while being energy-efficient. These models are based on core design principles of sparsity and asynchrony and take inspiration from biology/neuroscience.

Research projects

• EVENTS (Energy-efficient distributed sensor-systems for machine vision: event-based distributed AI algorithms) started in October 2022 as part of the funding program "BMBF - OCTOPUS - Electronic systems for trustworthy and energy-efficient decentralised dataprocessing in edge-computing". The grant aims to develop efficient general-purpose AI algorithms that can be adapted for deployment on energy-efficient neuromorphic systems for computer vision. The project consortium led by TU Dresden will implement and test the algorithms developed at INI, RUB on innovative neuromorphic hardware in various pilot applications. For more information see: the EVENTS project website.
• ESCADE (Energy-Efficient Large-Scale Artificial Intelligence for Sustainable Data Centers) started in May 2023. The aim of the project is to develop state-of-the art large, distributed and energy-efficient machine learning models for complex applications such as natural language processing. The project started in May 2023 and is funded by the Bundesministerium für Wirtschaft und Klimaschutz (BMWK). For more information see: the ESCADE project website.