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Scenario Based Run-Time Switching for Adaptive CNN-Based Applications at the Edge.

, , , and . ACM Trans. Embed. Comput. Syst., 21 (2): 14:1-14:33 (2022)

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Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project., , , , , , , , , and 9 other author(s). ICM, page 52-55. IEEE, (2018)ALOHA: an architectural-aware framework for deep learning at the edge., , , , , , , , , and 9 other author(s). INTESA@ESWEEK, page 19-26. ACM, (2018)Buffer Sizes Reduction for Memory-efficient CNN Inference on Mobile and Embedded Devices., and . DSD, page 133-140. IEEE, (2020)Memory-Throughput Trade-off for CNN-Based Applications at the Edge., and . ACM Trans. Design Autom. Electr. Syst., 28 (1): 2:1-2:26 (January 2023)Energy-Efficient and High-Throughput CNN Inference on Embedded CPUs-GPUs MPSoCs., , and . SAMOS, volume 13227 of Lecture Notes in Computer Science, page 127-143. Springer, (2021)Optimization and deployment of CNNs at the edge: the ALOHA experience., , , , , , , , , and 8 other author(s). CF, page 326-332. ACM, (2019)Scenario Based Run-Time Switching for Adaptive CNN-Based Applications at the Edge., , , and . ACM Trans. Embed. Comput. Syst., 21 (2): 14:1-14:33 (2022)Combining Task- and Data-Level Parallelism for High-Throughput CNN Inference on Embedded CPUs-GPUs MPSoCs., , and . SAMOS, volume 12471 of Lecture Notes in Computer Science, page 18-35. Springer, (2020)