Deep Dive Workshop
Resource-Aware & Constrained AI
Organized by:
Friday 9 October 10.05
Lead organizer: Emil Njor, Assistant Professor, University of Southern Denmark
The environmental footprint of AI and reliance on cloud computing limit its sustainability and real-world applicability, particularly for use cases with strict privacy requirements (such as pharmaceuticals) or for applications lacking reliable connectivity (such as remote wildlife monitoring).
To address this, developing resource-aware & constrained AI is crucial. Our 180-minute session strategically merges the Danish Resource-Aware AI and Edge AI communities. Backed by the DIREC workstream on “CPS, IoT and Autonomous Systems” and P1 Program on “Green AI”, we aim to eliminate fragmentation, foster deep collaboration, and position Denmark as a global leader in sustainable, decentralized AI.
We assume a holistic, systems-level view of the AI landscape, recognizing that robust AI goes beyond model architectures to encompass data preparation, hardware-software co-design, and execution.
Lightning Research Talks (80 mins)
8 presentations (8 mins + 2 min Q&A), presenting either research, research ideas, or short tool presentations.
Coffee Break (20 mins)
A brief recess to refresh before the interactive segments.
Panel Discussion (50 mins)
A moderated debate on the future of Resource-Aware and Constrained AI.
Structured Networking (30 mins)
Participants will be split into targeted discussion groups based on their core interests (e.g., hardware, algorithms, societal impact) to foster cross-disciplinary networking and ideate future joint projects.
For Research Talks:
Luis Landa – Research Assistant – IT University of Copenhagen: Solar-powered Computing
Josephine Plass-Wolter – PhD Student – University of Southern Denmark: AI & Sustainability in Health
Purbak Sengupta – MSc Student – Aarhus University: Predict Less, Know More: Event-First AI for Resource-Aware Wind Turbine Monitoring
Mikkel Dahl – MSc Student – University of Copenhagen: CoDeQ+: Joint Quantization and Pruning for Extreme Structured Sparsity
Alessandro Cerioli – PostDoc – Technical University of Denmark: Neural Network Optimization for Predictable Real-Time Edge Devices
Roberto Morabito – Associate Professor – Technical University of Denmark: [Title to be confirmed]
For the Panel Discussion:
Jan Madsen – Head of Department – DTU Compute
Vivek Shah – Samsung
Tilmann Rabl – Professor – Hasso Plattner Institute (Germany)
Intermediate: For attendees who have basic understanding or some experience with the subject but are not yet advanced.