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Dr. D. (Dolly) Sapra

Faculty of Science
Informatics Institute
Visiting address
  • Science Park 904
  • Room number: L5.47
Postal address
  • Postbus 94323
    1090 GH Amsterdam
Contact details
  • Publications

    2023

    • Aghapour, E., Sapra, D., Pimentel, A. D., & Pathania, A. (2023). PELSI: Power-Efficient Layer-Switched Inference. In 2023 IEEE 29th International Conference on Embedded and Real-Time Computing Systems and Applications: RTCSA 2023 : Niigata, Japan, 30 August -1 September 2023 : proceedings (pp. 12-17). IEEE Computer Society. https://doi.org/10.1109/RTCSA58653.2023.00011 [details]
    • Sapra, D., & Pimentel, A. D. (2023). Exploring Multi-core Systems with Lifetime Reliability and Power Consumption Trade-offs. In C. Silvano, C. Pilato, & M. Reichenbach (Eds.), Embedded Computer Systems: Architectures, Modeling, and Simulation: 23rd International Conference, SAMOS 2023, Samos, Greece, July 2–6, 2023 : proceedings (pp. 72–87). (Lecture Notes in Computer Science; Vol. 14385). Springer. https://doi.org/10.1007/978-3-031-46077-7_6 [details]

    2022

    • Aghapour, E., Sapra, D., Pimentel, A., & Pathania, A. (2022). CPU-GPU Layer-Switched Low Latency CNN Inference. In H. Fabelo, S. Ortega, & A. Skavhaug (Eds.), 2022 25th Euromicro Conference on Digital System Design: DSD 2022 : 31 August-2 September 2022, Maspalomas, Spain : proceedings (pp. 324-331). IEEE Computer Society. https://doi.org/10.1109/DSD57027.2022.00051 [details]
    • Minakova, S., Sapra, D., Stefanov, T., & Pimentel, A. D. (2022). Scenario Based Run-time Switching for Adaptive CNN-based Applications at the Edge. ACM Transactions on Embedded Computing Systems, 21(2), Article 14. Advance online publication. https://doi.org/10.1145/3488718 [details]
    • Sapra, D., & Pimentel, A. D. (2022). Designing convolutional neural networks with constrained evolutionary piecemeal training. Applied Intelligence, 52(15), 17103–17117. https://doi.org/10.1007/s10489-021-02679-7 [details]

    2021

    • Odyurt, U., Sapra, D., & Pimentel, A. D. (2021). The Choice of AI Matters: Alternative Machine Learning Approaches for CPS Anomalies. In H. Fujita, A. Selamat, JC.-W. Lin, & M. Ali (Eds.), Advances and Trends in Artificial Intelligence : From Theory to Practice: 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, Kuala Lumpur, Malaysia, July 26–29, 2021 : proceedings (Vol. II, pp. 474-484). (Lecture Notes in Computer Science; Vol. 12799), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-79463-7_40 [details]
    • van Ipenburg, I., Sapra, D., & Pimentel, A. D. (2021). Exploring Cell-Based Neural Architectures for Embedded Systems. In M. Kamp, I. Koprinska, A. Bibal, T. Bouadi, B. Frénay, L. Galárraga, J. Oramas, & L. Adilova (Eds.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2021, virtual event, September 13-17, 2021 : proceedings (Vol. I, pp. 363–374). (Communications in Computer and Information Science; Vol. 1524). Springer. https://doi.org/10.1007/978-3-030-93736-2_28 [details]

    2020

    • Sapra, D., & Pimentel, A. D. (2020). An evolutionary optimization algorithm for gradually saturating objective functions. In GECCO'20: proceedings of the 2020 Genetic and Evolutionary Computation Conference : July 8-12, 2020, Cancún, Mexico (pp. 886-893). Association for Computing Machinery. https://doi.org/10.1145/3377930.3389834 [details]
    • Sapra, D., & Pimentel, A. D. (2020). Constrained evolutionary piecemeal training to design convolutional neural networks. In H. Fujita, P. Fournier-Viger, M. Ali, & J. Sasaki (Eds.), Trends in Artificial Intelligence Theory and Applications : Artificial Intelligence Practices: 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Kitakyushu, Japan, September 22-25, 2020 : proceedings (pp. 709-721). (Lecture Notes in Computer Science; Vol. 12144), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-55789-8_61 [details]
    • Sapra, D., & Pimentel, A. D. (2020). Deep Learning Model Reuse and Composition in Knowledge Centric Networking. In ICCCN 2020: the 29th International Conference on Computer Communication and Networks : final program : August 3-August 6, 2020, Honolulu, Hawaii, USA (pp. 716-726). (Proceedings International Conference on Computer Communications and Networks; Vol. 29). IEEE. https://doi.org/10.1109/ICCCN49398.2020.9209668 [details]

    2019

    • Meloni, P., Loi, D., Busia, P., Deriu, G., Pimentel, A. D., Sapra, D., Stefanov, T., Minakova, S., Conti, F., Benini, L., Pintor, M., Biggio, B., Moser, B., Shepeleva, N., Fragoulis, N., Theodorakopoulos, I., Masin, M., & Palumbo, F. (2019). Optimization and deployment of CNNs at the Edge: The ALOHA experience. In ACM International Conference on Computing Frontiers 2019 (CF 2019) : proceedings : April 30-May 2, 2019, Alghero, Sardinia, Italy (pp. 326-332). Association for Computing Machinery. https://doi.org/10.1145/3310273.3323435 [details]

    2018

    • Meloni, P., Loi, D., Deriu, G., Pimentel, A. D., Sapra, D., Moser, B., Shepeleva, N., Conti, F., Benini, L., Ripolles, O., Solans, D., Pintor, M., Biggio, B., Stefanov, T., Minakova, S., Fragoulis, N., Theodorakopoulos, I., Masin, M., & Palumbo, F. (2018). ALOHA: an architectural-aware framework for deep learning at the edge. In M. Martina, & W. Fornanciari (Eds.), INTelligent Embedded Systems Architectures and Applications (INTESA): workshop proceedings 2018 : October 4, 2018, Torino, Italy (pp. 19-26). The Association for Computing Machinery. https://doi.org/10.1145/3285017.3285019 [details]
    • Meloni, P., Loi, D., Deriu, G., Pimentel, A. D., Sapra, D., Pintor, M., Biggio, B., Ripolles, O., Solans, D., Conti, F., Benini, L., Stefanov, T., Minakova, S., Moser, B., Shepeleva, N., Masin, M., Palumbo, F., Fragoulis, N., & Theodorakopoulos, I. (2018). Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project. In Proceeding of 2018 30th International Conference on Microelectronics (pp. 52-55). IEEE. https://doi.org/10.1109/ICM.2018.8704093 [details]

    2023

    2017

    • Sapra, D., & Altmeyer, S. (2017). Work In Progress: Design-Space Exploration of Multi-core Processors for Safety-Critical Real-Time Systems. In 2017 IEEE Real-Time Systems Symposium: proceedings : 5-8 December 2017, Paris, France (pp. 360-362). IEEE Computer Society. https://doi.org/10.1109/RTSS.2017.00040 [details]

    Prize / grant

    • Sapra, D. (2020). Best Paper Award.

    2022

    • Sapra, D. (2022). Efficient neural architectures for edge devices. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
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