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HAPPi-Net: Hardware-Aware Performant Perception of Neural Networks.. Technical University of Munich, Germany, (2021)Adversarial Robust Model Compression Using In-Train Pruning., , , , , , , , , and 2 other author(s). CVPR Workshops, page 66-75. Computer Vision Foundation / IEEE, (2021)Mind the Scaling Factors: Resilience Analysis of Quantized Adversarially Robust CNNs., , , , , , , and . DATE, page 706-711. IEEE, (2022)Binary-LoRAX: Low-Latency Runtime Adaptable XNOR Classifier for Semi-Autonomous Grasping with Prosthetic Hands., , , , , , , , , and 2 other author(s). ICRA, page 13430-13437. IEEE, (2021)Pruning CNNs for LiDAR-based Perception in Resource Constrained Environments., , , , , , , , and . IV Workshops, page 228-235. IEEE, (2021)The ZuSE-KI-Mobil AI Accelerator SoC: Overview and a Functional Safety Perspective., , , , , , , , , and 8 other author(s). DATE, page 1-6. IEEE, (2023)WinoTrain: Winograd-Aware Training for Accurate Full 8-bit Convolution Acceleration., , , , , , , and . DAC, page 1-6. IEEE, (2023)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)