Mahdieh Grailoo

Mahdieh Grailootanha, Jose Luis Nunez-Yanez,  Heterogeneous Edge Computing for Molecular Property Prediction with Graph Convolutional Networks, Electronics 14:101 (2025)  https://doi.org/10.3390/electronics14010101

Mahdieh Grailootanha, Tooraj Nikoubin, Jose Luis Nunez-Yanez,  Hardware Acceleration of Molecular Property Graph Prediction on a Heterogeneous Edge Platform, 2024 IEEE NORDIC CIRCUITS AND SYSTEMS CONFERENCE, NORCAS, IEEE (2024)  https://doi.org/10.1109/NorCAS64408.2024.10752459

Mahdieh Grailootanha, Tooraj Nikoubin, Oscar Gustafsson, Jose Luis Nunez-Yanez,  Activation Function Integration for Accelerating Multi-Layer Graph Convolutional Neural Networks, 17TH IEEE DALLAS CIRCUITS AND SYSTEMS CONFERENCE, DCAS 2024, Proceedings of the IEEE Dallas Circuits and Systems Workshop, IEEE (2024)  https://doi.org/10.1109/DCAS61159.2024.10539892

Hadi Mousanejad Jeddi, Mahdieh Grailootanha, Jose Luis Nunez-Yanez,  Leveraging Dynamic Range Analysis for Efficient Post-Training Quantization in Graph Convolutional Networks, 2024 IEEE NORDIC CIRCUITS AND SYSTEMS CONFERENCE, NORCAS, IEEE (2024)  https://doi.org/10.1109/NorCAS64408.2024.10752486

Olle Hansson, Mahdieh Grailootanha, Oscar Gustafsson, Jose Luis Nunez-Yanez,  Deep Quantization of Graph Neural Networks with Run-Time Hardware-Aware Training, APPLIED RECONFIGURABLE COMPUTING. ARCHITECTURES, TOOLS, AND APPLICATIONS, ARC 2024, Lecture Notes in Computer Science, pp. 33-47, SPRINGER INTERNATIONAL PUBLISHING AG (2024)  https://doi.org/10.1007/978-3-031-55673-9_3