@inproceedings{LasbyZimmerPokuttaetal.2025, author = {Lasby, Mike and Zimmer, Max and Pokutta, Sebastian and Schultheis, Erik}, title = {Compressed sparse tiles for memory-efficient unstructured and semi-structured sparsity}, booktitle = {Proceedings of the ICLR Workshop on Sparsity in LLMs}, year = {2025}, language = {en} } @inproceedings{FukudaKeraKawamoto2025, author = {Fukuda, Takuma and Kera, Hiroshi and Kawamoto, Kazuhiko}, title = {Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning}, booktitle = {2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, doi = {10.1109/CVPR52734.2025.00460}, pages = {4884 -- 4893}, year = {2025}, language = {en} } @inproceedings{HaaseHanelPokutta2025, author = {Haase, Jennifer and Hanel, Paul H. P. and Pokutta, Sebastian}, title = {S-DAT: a multilingual, GenAI-driven framework for automated divergent thinking assessment}, volume = {8}, booktitle = {Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES)}, pages = {1194 -- 1205}, year = {2025}, language = {en} } @inproceedings{VillimNguyenBobiketal.2025, author = {Villim, J{\´a}n and Nguyen, Martin and Bobik, Pavol and Genci, Jan and Gec{\´a}šek, Daniel}, title = {New Version of the COR Simulation Engine}, volume = {501}, booktitle = {Proceedings of 39th International Cosmic Ray Conference — PoS(ICRC2025)}, publisher = {Sissa Medialab}, address = {Trieste, Italy}, doi = {https://doi.org/10.22323/1.501.1377}, year = {2025}, abstract = {The COR simulation engine is a tool devoted to evaluating cosmic ray trajectories in Earth's magnetosphere. It is part of the COR System, available at https://cor.crmodels.org, and it also functions as a standalone command line tool. The former tool was published in 2022. We report the new version of the tool status with improved performance, precision, new functions/features, and refactored code.}, language = {en} } @inproceedings{ScheltenChristgauSchulteetal.2026, author = {Schelten, Niklas and Christgau, Steffen and Schulte, Anton and Schnor, Bettina and Signer, Hannes and Stabernack, Benno}, title = {A Flexible Open-Source Framework for FPGA-based Network-Attached Accelerators using SpinalHDL}, booktitle = {Architecture of Computing Systems - 39th International Conference, ARCS 2026, Mainz, Germany, March 24-26, 2026, Proceedings.}, year = {2026}, abstract = {Domain-specific accelerators are increasingly vital in heterogeneous computing systems, driven by the demand for higher computational capacity and especially energy efficiency. Network-attached FPGAs promise a scalable and flexible alternative to closely coupled FPGAs for integrating accelerators into computing environments. While the advantages of specialized hardware implementations are apparent, traditional hardware development and integration remain time-consuming and complex. We present an open-source framework which combines a hardware shell with supporting software libraries, which enables fast development and deployment of FPGA-based network-attached accelerators. In contrast to traditional approaches using VHDL or Verilog, we leverage generative programming with SpinalHDL, providing a flexible hardware description with multi-level abstractions. This work eases the integration of accelerators into existing network infrastructures and simplifies adaptation to different FPGAs, eliminating complex and lengthy top-level hardware descriptions.}, language = {en} } @inproceedings{GonnermannMuellerSahlingHaase2025, author = {Gonnermann-M{\"u}ller, Jana and Sahling, Kristina and Haase, Jennifer}, title = {Let's Be Realistic: AI-Recommender Use in a Complex Management Setting}, booktitle = {CHI EA '25: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems}, doi = {10.1145/3706599.3720131}, year = {2025}, language = {en} } @inproceedings{ScheltenChristgauHutzleretal.2026, author = {Schelten, Niklas and Christgau, Steffen and Hutzler, Merit and Kreowsky, Philipp and De Lucia, Marco and Schnor, Bettina and Signer, Hannes and Spazier, Johannes and Stabernack, Benno and Yahdzhyiev, Serhii}, title = {Using FPGA-based Network-Attached Accelerators for Energy-Efficient AI Training in HPC Datacenters}, booktitle = {2026 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}, year = {2026}, abstract = {FPGA-based Network-Attached Accelerators offer a disaggregated alternative to closely-coupled FPGAs or GPUs, but their adoption is very limited due to missing hardware/software frameworks. However, the usability and energy-efficiency of FPGAs for HPC use-cases has been demonstrated previously. Yet, the support by HPC infrastructure for those devices is lacking behind other accelerators. This paper addresses this shortcoming and demonstrates a full-stack approach that seamlessly integrates Network-Attached Accelerators in HPC datacenters and allows flexible and scalable usage of FPGAs. The presented work includes the according framework, integration steps and a show-case application from the geoscience domain. We evaluate our approach by comparing the training of a Physics-Informed Neural Network on the NAA against two GPU implementations. The NAA reduces total energy consumption by about 50\% and 17\% compared to the Keras and CUDA implementation, respectively. These results demonstrate that FPGA-based NAAs can be successfully integrated into HPC environments and are a viable path toward more energy-efficient AI training.}, language = {en} } @inproceedings{SunkaraRostamivonTycowiczetal.2026, author = {Sunkara, Vikram and Rostami, Atefe and von Tycowicz, Christoph and Sch{\"u}tte, Christof}, title = {Stop throwing away your Decoder; extract the learnt local coordinate system using Latent-XAI}, booktitle = {The 4th World Conference on Explainable Artificial Intelligence (XAI-2026)}, year = {2026}, language = {en} }