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

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

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