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Memory-Throughput Trade-off for CNN-Based Applications at the Edge.

, и . ACM Trans. Design Autom. Electr. Syst., 28 (1): 2:1-2:26 (января 2023)

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Svetlana Begel

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Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project., , , , , , , , , и 9 other автор(ы). ICM, стр. 52-55. IEEE, (2018)ALOHA: an architectural-aware framework for deep learning at the edge., , , , , , , , , и 9 other автор(ы). INTESA@ESWEEK, стр. 19-26. ACM, (2018)Memory-Throughput Trade-off for CNN-Based Applications at the Edge., и . ACM Trans. Design Autom. Electr. Syst., 28 (1): 2:1-2:26 (января 2023)Energy-Efficient and High-Throughput CNN Inference on Embedded CPUs-GPUs MPSoCs., , и . SAMOS, том 13227 из Lecture Notes in Computer Science, стр. 127-143. Springer, (2021)Optimization and deployment of CNNs at the edge: the ALOHA experience., , , , , , , , , и 8 other автор(ы). CF, стр. 326-332. ACM, (2019)ALOHA: A Unified Platform-Aware Evaluation Method for CNNs Execution on Heterogeneous Systems at the Edge., , , , и . IEEE Access, (2021)Buffer Sizes Reduction for Memory-efficient CNN Inference on Mobile and Embedded Devices., и . DSD, стр. 133-140. IEEE, (2020)Scenario Based Run-Time Switching for Adaptive CNN-Based Applications at the Edge., , , и . 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., , и . SAMOS, том 12471 из Lecture Notes in Computer Science, стр. 18-35. Springer, (2020)