PUB - Publikationen an der Universität Bielefeld

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• [9]
  

  2024 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2993643

  Porrmann, F., Hagemeyer, J., & Porrmann, M. (2024). HLS-Based Large Scale Self-Organizing Feature Maps. IEEE Access, 12, 142459-142474. https://doi.org/10.1109/ACCESS.2024.3471471

  PUB | PDF | DOI | WoS
   
• [8]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2983518

  Mika, K., Porrmann, F., Kucza, N., Griessl, R., & Hagemeyer, J. (2023). RECS: A Scalable Platform for Heterogeneous Computing. 2023 IEEE 36th International System-on-Chip Conference (SOCC), 1-6. Piscataway, NJ: IEEE. https://doi.org/10.1109/SOCC58585.2023.10256982

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• [7]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2979799

  Griessl, R., Porrmann, F., Kucza, N., Mika, K., Hagemeyer, J., Kaiser, M., Porrmann, M., et al. (2023). Evaluation of heterogeneous AIoT Accelerators within VEDLIoT. 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), 1-6. IEEE. https://doi.org/10.23919/DATE56975.2023.10137021

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• [6]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982048

  Mika, K., Griessl, R., Kucza, N., Porrmann, F., Kaiser, M., Tigges, L., Hagemeyer, J., et al. (2023). VEDLIoT. Next generation accelerated AIoT systems and applications. CF '23: Proceedings of the 20th ACM International Conference on Computing Frontiers, 291-296. New York, NY: ACM. https://doi.org/10.1145/3587135.3592175

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• [5]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2964196

  Kaiser, M., Griessl, R., Kucza, N., Haumann, C., Tigges, L., Mika, K., Hagemeyer, J., et al. (2022). VEDLIoT: Very Efficient Deep Learning in IoT. In Institut of Electrical and Electronics Engineers (IEEE) (Ed.), DATE '22: Proceedings of the 2022 Conference & Exhibition on Design, Automation & Test in Europe (pp. 963-968). Leuven: European Design and Automation Association. https://doi.org/10.23919/DATE54114.2022.9774653

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• [4]
  

  2021 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2957481

  Porrmann, F., Pilz, S., Stella, A., Kleinjohann, A., Denker, M., Hagemeyer, J., & Rückert, U. (2021). Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation. Frontiers in Neuroinformatics, 15(15), 723406. https://doi.org/10.3389/fninf.2021.723406

  PUB | PDF | DOI | WoS | PubMed | Europe PMC
   
• [3]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2941646

  Pilz, S., Porrmann, F., Kaiser, M., Hagemeyer, J., Hogan, J. M., & Rückert, U. (2020). Accelerating Binary String Comparisons with a Scalable, Streaming-Based System Architecture Based on FPGAs. Algorithms, 13(2), 47. doi:10.3390/a13020047

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• [2]
  

  2018 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2918788

  Kaiser, M., Pilz, S., Porrmann, F., Hagemeyer, J., & Porrmann, M. (2018). Accelerating Hamming Distance Comparisons for Locality Sensitive Hashing (LSH) using FPGAs. 12th CeBiTec Symposium - Big Data in Medicine and Biotechnology - Abstract Book, 12, 48-49. Bielefeld.

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• [1]
  

  2017 | Kurzbeitrag Konferenz / Poster | Veröffentlicht | PUB-ID: 2918683

  Kaiser, M., Griessl, R., Hagemeyer, J., Jungewelter, D., Porrmann, F., Pilz, S., Porrmann, M., et al. (2017). A Reconfigurable Heterogeneous Microserver Architecture for Energy-efficient Computing. Third International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC'17) Denver, CO.

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