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Hardware Aware Robust Compression of Neural Networks.. Technical University of Munich, Germany, (2023)Mind the Scaling Factors: Resilience Analysis of Quantized Adversarially Robust CNNs., , , , , , , and . DATE, page 706-711. IEEE, (2022)Adversarial Robust Model Compression Using In-Train Pruning., , , , , , , , , and 2 other author(s). CVPR Workshops, page 66-75. Computer Vision Foundation / IEEE, (2021)ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks., , , , , , and . DAC, page 1-6. IEEE, (2020)AnaCoNGA: Analytical HW-CNN Co-Design Using Nested Genetic Algorithms., , , , , , , , , and 3 other author(s). DATE, page 238-243. IEEE, (2022)BreakingBED: Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks., , , , , , , , , and 1 other author(s). IntelliSys (1), volume 294 of Lecture Notes in Networks and Systems, page 148-167. Springer, (2021)Hardware-Aware Mixed-Precision Neural Networks using In-Train Quantization., , , , , , , , and . BMVC, page 60. BMVA Press, (2021)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)An Efficient FPGA Accelerator Design for Optimized CNNs Using OpenCL., , and . ARCS, volume 11479 of Lecture Notes in Computer Science, page 236-249. Springer, (2019)Investigating Binary Neural Networks for Traffic Sign Detection and Recognition., , , , , , , and . IV, page 1400-1405. IEEE, (2021)