Search results
Results: 24
Number of items: 24
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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 -
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. https://doi.org/10.1145/3488718
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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
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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
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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
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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
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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
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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
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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
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