Edge AI Patterns
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-45140-9_9
Reference10 articles.
1. Ding, A.Y., Peltonen, E., Meuser, T., et al.: Roadmap for edge AI: a Dagstuhl perspective. ACM SIGCOMM Comput. Commun. Rev. 52(1), 28–33 (2022). https://doi.org/10.1145/3523230.3523235
2. Tziouvaras, A., Foukalas, F.: Edge AI for Industry 4.0: an Internet of Things approach. In: Proceedings of the 24th Pan-Hellenic Conference on Informatics, November 2020, pp. 121–126 (2021). https://doi.org/10.1145/3437120.3437289
3. Wu, Y., Zhang, L., Gu, Z., Lu, H.,Wan, S.: Edge-AI-driven framework with efficient mobile network design for facial expression recognition. ACM Trans. Embed. Comput. Syst. 22(3), 1–17 (2023). https://doi.org/10.1145/3587038. Article No. 57
4. Raith, P., Rausch, T., Prüller, P., Furutanpey, A., Dustdar, S.: An end-to-end framework for benchmarking edge-cloud cluster management techniques. In: IEEE International Conference on Cloud Engineering (IC2E), CA, USA, pp. 22–28 (2022). https://doi.org/10.1109/IC2E55432.2022.00010
5. Le, K., Le-Minh, K., Thai, H.: BrainyEdge: an AI-enabled framework for IoT edge computing. Inf. Commun. Technol. Express 9(2), 211–221 (2023). https://doi.org/10.1016/j.icte.2021.12.007
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