Hasso-Plattner-Institut25 Jahre HPI
Hasso-Plattner-Institut25 Jahre HPI


Towards Sustainable Digital Technologies

Artificial intelligence, blockchain, big data analysis and ubiquitous data exchange. Digital transformation permeates our everyday lives around the world and is the key to solving global human challenges, such as climate change, poverty and economic prosperity for all. However, is digitalization ecologically sustainable? No, currently it is not – but this has to change now!

What is the problem?

Innovative business models and the transformation of entire economic sectors are almost exclusively based on the use of AI, Big Data, Blockchain and globally interconnected data centers. The greatest potential for growth lies in two areas: digital services, through the consistent development of digital platforms and in manufacturing by leveraging the opportunities of the Internet of  things. Individual products and services can be offered at the price of mass goods, thus increasing global prosperity. However, it is often ignored that digital technologies are also increasingly
the cause of global pollution. Every digital operation consumes energy and therefore adds to the global CO2 footprint. Very soon, digitalization will become the climate problem number one.

Graphic: HPI clean IT - AI Carbon
Artificial intelligence emissions Training a modern AI model can use up as much carbon as 300 roundtrip flights from SFO-NYC or the life cycle of 5 cars incl. fuel. / Strubell et al. 2019

How clean-IT works

Graphic: HPI clean IT: How it works

To solve the paradox of more from less, the principle of “Sustainability by Design” needs to become the very foundation of software development. Often unnecessarily complicated or wasteful IT systems design causes higher energy consumption compared to algorithmic setups that are more efficient. Innovative IT architectures can achieve the same/slightly lower precision or data throughput, while saving enormous amounts of energy. Algorithmic efficiency therefore needs to become a leading paradigm of software development. We call this  approach clean-IT.

The clean-IT course series

The clean-IT course series was set up to gather and exchange approaches and solutions for the initiative. The aim of the openHPI course is to be an international exchange platform for research institutions, industry, and politics to discuss issues of sustainable digitization. 

The clean-IT course series is open to all people and groups that are concerned with reducing the energy requirements of digital technologies and with developing guidelines, algorithms, and procedures for this purpose. Everyone is invited to continuously present and share new findings, proposals and techniques on the platform. Already, researchers from HPI and other universities, as well as experts from associations and companies, present approaches to solutions for different areas of digitization. The HPI Sustainability Club also provides suggestions on how to save energy in everyday life.

clean-IT openXchange

In addition to the course series, ideas and solutions from the initiative are also presented in the clean-IT openXchange livetalk series. Once in every month, viewers can ask their questions directly to the experts, make suggestions and discuss together. The recordings of the talks will also be available afterwards. 

Click here to join the Zoom session (Meeting ID: 950 4264 6125 Passcode: 172895).

For further information on the live talks and regular announcements, you can join the clean-IT course series.

clean-IT Community Conference 2022

30 March 2022

The clean-IT Community Conference is an international platform for the exchange of ideas to make the digital world more sustainable. High-level representatives of the EU Commission and the German federal government engage in a dialogue with stakeholders of industry, academia and NGOs to discuss what needs to be done to reduce the carbon footprint of digitalization.

More information can be found here

Examples of clean-IT

Binary Neural Networks

Data Profiling with HPIValid

Heuristic Algorithms for Optimization of Submodular Functions

Energy-Aware Computing