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HW-FlowQ: A Multi-Abstraction Level HW-CNN Co-design Quantization Methodology.

, , , , , , , , , и . ACM Trans. Embed. Comput. Syst., 20 (5s): 66:1-66:25 (2021)

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Emanuele Bellini

Emanuele Grasso

Emanuele Stanga

Emanuele Ruffaldi

Emanuele Manca

 

Другие публикации лиц с тем же именем

Mind the Scaling Factors: Resilience Analysis of Quantized Adversarially Robust CNNs., , , , , , , и . DATE, стр. 706-711. IEEE, (2022)AnaCoNGA: Analytical HW-CNN Co-Design Using Nested Genetic Algorithms., , , , , , , , , и 3 other автор(ы). DATE, стр. 238-243. IEEE, (2022)HW-FlowQ: A Multi-Abstraction Level HW-CNN Co-design Quantization Methodology., , , , , , , , , и . ACM Trans. Embed. Comput. Syst., 20 (5s): 66:1-66:25 (2021)NLCMAP: A Framework for the Efficient Mapping of Non-Linear Convolutional Neural Networks on FPGA Accelerators., , , , , , и . ICIP, стр. 926-930. IEEE, (2022)TEMET: Truncated REconfigurable Multiplier with Error Tuning., , , , и . ApplePies, том 1110 из Lecture Notes in Electrical Engineering, стр. 370-377. Springer, (2023)HW-Flow: A Multi-Abstraction Level HW-CNN Codesign Pruning Methodology., , , , , , , , , и . Leibniz Trans. Embed. Syst., 8 (1): 03:1-03:30 (2022)ERODE: Error Resilient Object DetEction by Recovering Bounding Box and Class Information., , , , и . PRIME, стр. 277-280. IEEE, (2023)MARLIN: A Co-Design Methodology for Approximate ReconfigurabLe Inference of Neural Networks at the Edge., , , , и . IEEE Trans. Circuits Syst. I Regul. Pap., 71 (5): 2105-2118 (мая 2024)