PUB - Publikationen an der Universität Bielefeld

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• [10]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982048

  Mika, K., et al., 2023. VEDLIoT. Next generation accelerated AIoT systems and applications. In CF '23: Proceedings of the 20th ACM International Conference on Computing Frontiers. New York, NY: ACM, pp. 291-296.

  PUB | DOI
   
• [9]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2979799

  Griessl, R., et al., 2023. Evaluation of heterogeneous AIoT Accelerators within VEDLIoT. In 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, pp. 1-6.

  PUB | DOI
   
• [8]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2964196

  Kaiser, M., 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. Leuven: European Design and Automation Association, pp. 963-968.

  PUB | DOI
   
• [7]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982047

  Salami, B., et al., 2020. LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing. In 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). Piscataway, NJ: IEEE, pp. 169-174.

  PUB | DOI
   
• [6]
  

  2020 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2941646

  Pilz, S., et al., 2020. Accelerating Binary String Comparisons with a Scalable, Streaming-Based System Architecture Based on FPGAs. Algorithms, 13(2): 47.

  PUB | PDF | DOI | Download (ext.) | WoS
   
• [5]
  

  2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982045

  Cristal, A., et al., 2018. LEGaTO. First steps towards energy-efficient toolset for heterogeneous computing. In SAMOS '18. Proceedings of the 18th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation. New York, NY: ACM, pp. 210-217.

  PUB | DOI
   
• [4]
  

  2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2979448

  Cristal, A., et al., 2018. LEGaTO. First steps towards energy-efficient toolset for heterogeneous computing. In T. Mudge, ed. Proceedings of the 18th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation. New York, NY, USA: ACM, pp. 210-217.

  PUB | DOI
   
• [3]
  

  2018 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2955564

  Cristal, A., et al., 2018. LEGaTO Project: Towards Energy-Efficient, Secure, Fault-tolerant Toolset for Heterogeneous Computing. In D. Kaeli, ed. Proceedings of the 15th ACM International Conference on Computing Frontiers. New York, NY: ACM, pp. 276-278.

  PUB | DOI | Download (ext.)
   
• [2]
  

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

  Kaiser, M., et al., 2018. Accelerating Hamming Distance Comparisons for Locality Sensitive Hashing (LSH) using FPGAs. In 12th CeBiTec Symposium - Big Data in Medicine and Biotechnology - Abstract Book. no.12 Bielefeld, pp. 48-49.

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• [1]
  

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

  Kaiser, M., et al., 2017. A Reconfigurable Heterogeneous Microserver Architecture for Energy-efficient Computing. In Third International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC'17). Denver, CO.

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