Sustainable Machine Learning
With Sustainable Machine Learning, you don’t have to choose between cutting-edge technology and protecting the planet. Our Sustainable Machine Learning team optimizes every stage— from algorithm design to deployment—minimizing environmental impact while maximizing results.
Our Sustainable Machine Learning projects are designed to achieve the following key goals:
- Energy Efficiency: Reducing energy consumption by designing energy-efficient neural networks, improving software engineering processes, model optimization, using low-power smart hardware such as microcontrollers, etc.
- Carbon Reduction: Minimizing the carbon footprint by creating distributed AI in the cloud-to-edge continuum and deploying on edge devices, etc.
- Sustainable Principles: Building machine learning solutions that align with global sustainability goals, particularly the United Nations’ Sustainable Development Goals (SDGs). Every project is guided by the principles of responsible AI and environmental stewardship, ensuring that our work not only advances technological innovation but also contributes to a healthier planet.
How to Get Involved
We are always looking for collaborators passionate about Sustainable Machine Learning. Whether you're an academic, a data scientist, an industry leader, or a stakeholder, your contributions can help push the boundaries of eco-friendly AI.
Collaboration Opportunities
- Research Partnerships: Join forces with our research team to co-develop sustainable AI models and contribute to publications in leading academic journals and conferences.
- Industry Collaborations: Partner with us to integrate sustainable machine learning solutions into your business, reducing both costs and environmental impact.
- Funding Opportunities: Support our mission to create a sustainable AI future through grants and sponsorships.
Get in Touch
For inquiries, collaborations, or more information about our sustainable machine learning projects, please contact us:
Projects
MSc theses
2017 Andreas Ternstedt, Pattern recognition with spiking neural networks and the ROLLS low-power online learning neuromorphic processor, link to thesis
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2018 Mattias Nilsson, Monte Carlo Optimization of Neuromorphic Cricket Auditory Feature Detection Circuits in the Dynap-SE Processor, link to thesis External link.
2019 Oskar Öberg, Critical Branching Regulation of the E-I Net Spiking Neural Network Model, link to thesis External link., collaboration External link.
2021 Olof Johansson, Training of Object Detection Spiking Neural Networks for Event-Based Vision, link to thesis External link.
2022 Kim Petersson Steenari, A neuromorphic approach for edge use allocation, link to thesis External link., collaboration External link.
PhD theses
2023 Mattias Nilsson Event-Driven Architectures for Heterogeneous Neuromorphic Computing Systems link External link.
Educational Resources
Neuromorphic Computing D7064E
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Universeh (EU funding- European Space University for Earth and Humanity)
Together, we can harness the power of AI to create a more sustainable and environmentally conscious future. Explore, innovate, and collaborate with us on Sustainable Machine Learning Projects!
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