Auto-DOK: Compiler-Assisted Automatic Detection of Offload Kernels for FPGA-HBM Architectures. In Smail Niar, Hamza Ouarnoughi, and Amund Skavhaug (Eds.), Proceedings - 2023 26th Euromicro Conference on Digital System Design, DSD 2023, 577--584, Institute of Electrical and Electronics Engineers Inc., United States of America, 2023. [PUMA: topic_federatedlearn xack] URL
SetQuence & SetOmic: Deep Set Transformer-based Representations of Cancer Multi-Omics. 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2022, 139--147, IEEE, New York u. a., United States of America, 2022. [PUMA: nopdf topic_federatedlearn]
SetQuence & SetOmic: Deep set transformers for whole genome and exome tumour analysis. BioSystems, (235)Elsevier, January 2024. [PUMA: topic_federatedlearn yaff]
RTASS: a RunTime Adaptable and Scalable System for Network-on-Chip-Based Architectures. 2023 26th Euromicro Conference on Digital System Design (DSD), 585--592, IEEE, Sep 8, 2023. [PUMA: nopdf topic_federatedlearn] URL
Compiler-Assisted Kernel Selection for FPGA-based Near-Memory Computing Platforms. Proceedings - 31st IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2023, 222, Institute of Electrical and Electronics Engineers Inc., United States of America, 2023. [PUMA: FIS_scads nopdf topic_federatedlearn] URL
ArcvaVX: OpenVX Framework for Adaptive Reconfigurable Computer Vision Architectures. In Francesca Palumbo, Georgios Keramidas, Nikolaos Voros, and Pedro C. Diniz (Eds.), Applied Reconfigurable Computing. Architectures, Tools, and Applications - 19th International Symposium, ARC 2023, Proceedings, 97--112, Springer Science and Business Media B.V., Germany, 2023. [PUMA: FIS_scads nopdf topic_federatedlearn] URL
Performance Estimation and Prototyping of Reconfigurable Near-Memory Computing Systems. In Nele Mentens, Nele Mentens, Leonel Sousa, Pedro Trancoso, Nikela Papadopoulou, and Ioannis Sourdis (Eds.), Proceedings - 2023 33rd International Conference on Field-Programmable Logic and Applications, FPL 2023, 357--358, Institute of Electrical and Electronics Engineers Inc., United States of America, Nov 2, 2023. [PUMA: nopdf topic_federatedlearn] URL
NC-Library: Expanding SystemC Capabilities for Nested reConfigurable Hardware Modelling. ACM Transactions on Reconfigurable Technology and Systems, (17)3:1--29, Association for Computing Machinery (ACM), New York, Apr 27, 2024. [PUMA: FIS_scads imported topic_federatedlearn xack yaff]
DECISION: Distributing OpenVX Applications on CPUs, GPUs and FPGAs using OpenCL. 84--91, May 2022. [PUMA: topic_software topic_federatedlearn imported]
NDP-RANK: Prediction and ranking of NDP systems performance using machine learning. Microprocessors and Microsystems, (96):104707, 2023. [PUMA: topic_federatedlearn Design Machine Modeling, Near-data Prediction, exploration learning, processing, space] URL
Schlussbericht der Anwendungspartner zum Verbundvorhaben: ScaDS – Competence Center for Scalable Data Services and Solutions Dresden/Leipzig -- Phase 2. May 2022. [PUMA: unit_transfer topic_federatedlearn]
Compiler-assisted kernel selection for FPGA-based near-memory computing platforms. 2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), IEEE, May 2023. [PUMA: topic_federatedlearn]
Performance estimation and prototyping of reconfigurable near-memory computing systems. 2023 33rd International Conference on Field-Programmable Logic and Applications (FPL), IEEE, September 2023. [PUMA: topic_federatedlearn]
SpheroScan: a user-friendly deep learning tool for spheroid image analysis. Gigascience, (12)Oxford University Press (OUP), December 2022. [PUMA: topic_federatedlearn 3D Image Mask R-CNN; analysis; deep high-throughput image learning; screening; segmentation spheroids;]
MLcps: machine learning cumulative performance score for classification problems. Gigascience, (12)December 2022. [PUMA: topic_federatedlearn Python classification evaluation evaluation; learning; machine model package; problems; score unified]
Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience, (13)January 2024. [PUMA: topic_federatedlearn AutoML; analysis; classification data learning; machine problems; visualization]