| VEDLIoT: very efficient deep learning in IoT M Kaiser, R Griessl, N Kucza, C Haumann, L Tigges, K Mika, ... 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 963-968, 2022 | 10 | 2022 |
| A computational-graph partitioning method for training memory-constrained DNNs F Qararyah, M Wahib, D Dikbayır, ME Belviranli, D Unat Parallel computing 104, 102792, 2021 | 8 | 2021 |
| Fibha: fixed budget hybrid CNN accelerator F Qararyah, MW Azhar, P Trancoso 2022 IEEE 34th International Symposium on Computer Architecture and High …, 2022 | 5 | 2022 |
| Evaluation of heterogeneous AIoT Accelerators within VEDLIoT R Griessl, F Porrmann, N Kucza, K Mika, J Hagemeyer, M Kaiser, ... 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), 1-6, 2023 | 3 | 2023 |
| ComScribe: identifying intra-node GPU communication P Akhtar, E Tezcan, FM Qararyah, D Unat International Symposium on Benchmarking, Measuring and Optimization, 157-174, 2020 | 3 | 2020 |
| A time efficient model for region of interest extraction in real time traffic signs recognition system F Qararyah, YA Daraghmi, E Daraghmi, S Rajora, CT Lin, M Prasad 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 83-87, 2018 | 2 | 2018 |
| An Efficient Hybrid Deep Learning Accelerator for Compact and Heterogeneous CNNs F Qararyah, MW Azhar, P Trancoso ACM Transactions on Architecture and Code Optimization 21 (2), 1-26, 2024 | 1 | 2024 |
| VEDLIoT: Next generation accelerated AIoT systems and applications K Mika, R Griessl, N Kucza, F Porrmann, M Kaiser, L Tigges, ... Proceedings of the 20th ACM International Conference on Computing Frontiers …, 2023 | 1 | 2023 |
| Initial report on the dl accelerator design MP UOS, GR UOS MT, P Trancoso, FM Qararyah, S Zouzoula | 1 | 2022 |
| Logistic Regression Based Model for Improving the Accuracy and Time Complexity of ROI's Extraction in Real Time Traffic Signs Recognition System F Qararyah, YA Daraghmi, EY Daraghmi Journal of Computer Science Research 1 (1), 10-15, 2019 | 1 | 2019 |
| Fusing Depthwise and Pointwise Convolutions for Efficient Inference on GPUs F Qararyah, MW Azhar, MA Maleki, P Trancoso arXiv preprint arXiv:2404.19331, 2024 | | 2024 |
| A Scalable, Heterogeneous Hardware Platform for Accelerated AIoT based on Microservers R Griessl, F Porrmann, N Kucza, K Mika, J Hagemeyer, M Kaiser, ... Shaping the Future of IoT with Edge Intelligence, 179, 2023 | | 2023 |
| VEDLIoT K Mika, R Griessl, N Kucza, F Porrmann, M Kaiser, L Tigges, ... Proceedings of the 20th ACM International Conference on Computing Frontiers, 2023 | | 2023 |
| Training Memory-Constrained Deep Learning Models Using Automatic Dataflow-Graph Partitioning FMF Qararyah | | 2020 |
| ComScribe: Identifying Intra-node GPU Communication FM Qararyah, D Unat | | |
Pedro TrancosoDepartment of Computer Science and Engineering, Chalmers University of TechnologyVerified email at chalmers.se
