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CNN hardware acceleration on a low-power and low-cost APSoC.

, , , , и . DASIP, стр. 7-12. IEEE, (2019)

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Gianfranco Walsh

Deriu Claudio

Gianfranco Gentile

Gianfranco Hagen

Gianfranco Pacchioni

 

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NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs., , , , , , , и . ACM Trans. Reconfigurable Technol. Syst., 11 (3): 18:1-18:24 (2018)Exploring NEURAghe: A Customizable Template for APSoC-Based CNN Inference at the Edge., , , , , , и . IEEE Embed. Syst. Lett., 12 (2): 62-65 (2020)Optimization and deployment of CNNs at the edge: the ALOHA experience., , , , , , , , , и 8 other автор(ы). CF, стр. 326-332. ACM, (2019)Flexible Acceleration of Convolutions on FPGAs: NEURAghe 2.0., , и . CPS Summer School, PhD Workshop, том 2457 из CEUR Workshop Proceedings, стр. 60-71. CEUR-WS.org, (2019)An Automated Design Flow for Adaptive Neural Network Hardware Accelerators., , , , , , , , и . J. Signal Process. Syst., 95 (9): 1091-1113 (сентября 2023)Target-Aware Neural Architecture Search and Deployment for Keyword Spotting., , , , , и . IEEE Access, (2022)Optimizing Temporal Convolutional Network Inference on FPGA-Based Accelerators., , , , и . IEEE J. Emerg. Sel. Topics Circuits Syst., 10 (3): 348-361 (2020)CNN hardware acceleration on a low-power and low-cost APSoC., , , , и . DASIP, стр. 7-12. IEEE, (2019)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)Task-Specific Automation in Deep Learning Processes., , , , , , , , , и 3 other автор(ы). DEXA Workshops, том 1479 из Communications in Computer and Information Science, стр. 159-169. Springer, (2021)