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Task-Specific Automation in Deep Learning Processes.

, , , , , , , , , , , , and . DEXA Workshops, volume 1479 of Communications in Computer and Information Science, page 159-169. Springer, (2021)

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Flexible Acceleration of Convolutions on FPGAs: NEURAghe 2.0., , and . CPS Summer School, PhD Workshop, volume 2457 of CEUR Workshop Proceedings, page 60-71. CEUR-WS.org, (2019)NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs., , , , , , , and . ACM Trans. Reconfigurable Technol. Syst., 11 (3): 18:1-18:24 (2018)Optimization and deployment of CNNs at the edge: the ALOHA experience., , , , , , , , , and 8 other author(s). CF, page 326-332. ACM, (2019)Target-Aware Neural Architecture Search and Deployment for Keyword Spotting., , , , , and . IEEE Access, (2022)Optimizing Temporal Convolutional Network Inference on FPGA-Based Accelerators., , , , and . IEEE J. Emerg. Sel. Topics Circuits Syst., 10 (3): 348-361 (2020)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)Task-Specific Automation in Deep Learning Processes., , , , , , , , , and 3 other author(s). DEXA Workshops, volume 1479 of Communications in Computer and Information Science, page 159-169. Springer, (2021)CNN hardware acceleration on a low-power and low-cost APSoC., , , , and . DASIP, page 7-12. IEEE, (2019)ALOHA: an architectural-aware framework for deep learning at the edge., , , , , , , , , and 9 other author(s). INTESA@ESWEEK, page 19-26. ACM, (2018)