Energy-Efficient AI: Models, Algorithms, and Hardware for Sustainable Intelligence
As artificial intelligence systems grow in scale and capability, their energy demands are increasing at an unsustainable pace. This workshop addresses the urgent need for energy-efficient AI, a field that seeks to develop environmentally responsible and computationally efficient approaches to machine learning. The goal is to explore innovations that reduce energy consumption across the AI stack — from models and learning algorithms to hardware implementations — while maintaining or even enhancing performance. One source of inspiration for reaching this goal is the brain, which manages to provide intelligence with 20W.

Call for Posters
The workshop will be inclusive and accessible to a broad audience across machine learning, computer architecture, and systems engineering. Topics include but are not limited to:
- Model compression and pruning techniques that reduce inference and training costs
- Quantization and low-precision arithmetic for efficient deployment
- New learning algorithms designed for reduced computational and memory overhead
- Architectures and model designs tailored for energy-aware operation
- Neuromorphic computing and spiking neural networks inspired by biological efficiency
- Hardware accelerators, including FPGAs, photonic processors, and neuromorphic chips, that offer novel energy-performance trade-offs
This workshop aims to bring together researchers and practitioners from diverse backgrounds to foster collaboration and cross-pollination of ideas in making AI more sustainable without compromising its transformative potential.
Submission Guidelines
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Format: NeurIPS style (use the linked official NeurIPS LaTeX template)
- Length: Up to 2 pages (excluding references, which may extend beyond the limit). Since the entire submission is an abstract, there is no need to use the abstract environment.
- Review: Submissions will be lightly reviewed for relevance and quality. Accepted abstracts will be selected for presentation as posters.
- Archival Policy: The workshop is non-archival. Authors are encouraged to submit work that is preliminary, in progress, or recently published elsewhere.
Important Dates
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Submission Deadline: October 23, 2025, AoE (extended)
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Accept/Reject Notifications: October 31, 2025, AoE
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Workshop: December 2, 2025
You can submit your extended abstract on the following email: Randi Goertz
Organizers: Wolfgang Maass, Marcel van Gerven
Workshop Agenda (Tentative)
The ELLIS UnConference workshop is co-located with EurIPS Copenhagen and will take place on December 2, 2025.
Time | Session |
8:00 - 9:00 | Registration |
9:00 - 10:30 | Workshop talks |
Angeliki Pantazi (IBM Zurich): Designing Energy-Efficient AI: Insights from Neural Systems | |
Emre Neftci (Forschungszentrum Jülich): Neuroscience-guided Learning Rules Discovery for Efficient AI | |
Wilfred van der Wiel (University of Twente): Reconfigurable nonlinear processing in silicon | |
10:30 - 11:00 | Coffee break |
11:00 - 12:30 | Workshop talks |
Shih-Chii Liu (ETH and University of Zurich): Brain-inspired dynamic sparsity for neuromorphic AI | |
Iason Chalas (ETH and IBM Zurich): Analog In-Memory Computing for Efficient Large Language Model Deployment | |
Nasir Ahmad (Radboud University Nijmegen): Two steps forward and no steps back: Training neural networks in noisy hardware without backward passes | |
12:30 - 13:30 | Lunch |
13:30 - 15:00 | Workshop talks |
Yukun Yang (TU Graz): A brain-inspired method for context-aware and explainable planning that is suitable for implementation in energy-efficient AI | |
Panel Discussion of the Workshop on the future of emergy-efficient AI in general, and in ELLIS | |
15:30 - 16:00 | ELLIS Unconference Welcoming Remarks |
16:00 - 18:00 | ELLIS Unconference Poster Session |
18:00 - 20:00 | ELLIS Unconference Reception |