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Task-Specific Automation in Deep Learning Processes

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Database and Expert Systems Applications - DEXA 2021 Workshops (DEXA 2021)


Recent advances in deep learning facilitate the training, testing, and deployment of models through so-called pipelines. Those pipelines are typically orchestrated with general-purpose machine learning frameworks (e.g., Tensorflow Extended), where developers manually call the single steps for each task-specific application. The diversity of task- and technology-specific requirements in deep learning projects increases the orchestration effort. There are recent advances to automate the orchestration with machine learning, which are however, still immature and do not support task-specific applications. Hence, we claim that partial automation of pipeline orchestration with respect to specific tasks and technologies decreases the overall development effort. We verify this claim with the ALOHA tool flow, where task-specific glue code is automated. The gains of the ALOHA tool flow pipeline are evaluated with respect to human effort, computing performance, and security.

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  1. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  2. Huang, Y., Chen, Y.: Autonomous driving with deep learning: a survey of state-of-art technologies CoRR,  abs/2006.06091(2020)

    Google Scholar 

  3. Accessed 16 Apr 2021

  4. Amershi, S., et al. (eds.): Software engineering for machine learning: a case study. In: Proceedings of the 41st International Conference on Software Engineering, Montreal, QC, Canada, May 25–31 2019, pp. 291–300. IEEE/ACM (2019)

    Google Scholar 

  5. Meloni, P., et al. (eds.): Optimization and deployment of CNNs at the edge: the ALOHA experience. In: Proceedings of the 16th ACM International Conference on Computing Frontiers (CF 2019), Alghero, Italy, April 30–-May 2 2019, pp. 326–332. ACM (2019)

    Google Scholar 

  6. John, M.M., Olsson, H.H., Bosch, J.: Developing ML/DL models : a design framework. In: International Conference on Software and Systems Process, Seoul, Republic of Korea (2020)

    Google Scholar 

  7., Accessed 16 Apr 2021

  8. Accessed 16 Apr 2021

  9. Cai, H., Gan, C., Han, S.: Once for all: train one network and specialize it for efficient deployment. ArXiv, abs/1908.09791 (2020)

    Google Scholar 

  10. Bosch, J., Crnkovic, I., Olsson, H.: Engineering AI systems: a research Agenda (2020)

    Google Scholar 

  11. Ehrlinger, L., Wöß, W.: Automated Data Quality Monitoring. In: Proceedings of the 22nd MIT International Conference on Information Quality. Little Rock, AR (2017)

    Google Scholar 

  12. Meloni, P., et al.: NEURAghe: exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on Zynq SoCs. ACM Trans. Reconfigurable Tech. Syst. 11(18), 1–18:24 (2018)

    Google Scholar 

  13. Arpteg, A., Brinne, B., Crnkovic-Friis, L., Bosch, J.: Software engineering challenges of deep learning. In: Proceedings of the 44th Euromicro Conference on Software Engineering and Advanced Applications, IEEE. abs/1810.12034 (2018)

    Google Scholar 

  14. Accessed 23 Apr 2021

  15. Drori, I., et al.: Machine learning pipeline synthesis. In: Proceedings of AutoML Workshop at ICML (2018)

    Google Scholar 

  16. Sculley, D., et al. (eds.): Hidden technical debt in machine learning systems. In: Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, Montreal, Quebec, Canada (2015)

    Google Scholar 

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This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No. 780788.

We want to thank the ALOHA Team Members for their contribution of their results and comments. Further, we want to thank Lisa Ehrlinger from SCCH for her critical eye to enforce scientific correctness and understandability.

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Correspondence to Gerald Czech .

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Buchgeher, G. et al. (2021). Task-Specific Automation in Deep Learning Processes. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2021 Workshops. DEXA 2021. Communications in Computer and Information Science, vol 1479. Springer, Cham.

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  • Print ISBN: 978-3-030-87100-0

  • Online ISBN: 978-3-030-87101-7

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