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APOLLO: An Automated Power Modeling Framework for Runtime Power Introspection in High-Volume Commercial Microprocessors., , , , , , , , , and . MICRO, page 1-14. ACM, (2021)AI-Powered IoT System at the Edge., , , , , , , and . CogMI, page 242-251. IEEE, (2021)CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification., , , , , and . DAC, page 1-6. IEEE, (2023)Fisher-aware Quantization for DETR Detectors with Critical-category Objectives., , , , , , , , and . CoRR, (2024)On Building Efficient and Robust Neural Network Designs., , , , and . IEEECONF, page 317-321. IEEE, (2022)Global Vision Transformer Pruning with Hessian-Aware Saliency., , , , , and . CVPR, page 18547-18557. IEEE, (2023)Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective., , , , , and . CVPR, page 9311-9319. Computer Vision Foundation / IEEE, (2021)SPN dash: fast detection of adversarial attacks on mobile via sensor pattern noise fingerprinting., , , , , and . ICCAD, page 132. ACM, (2018)Block-Wise Mixed-Precision Quantization: Enabling High Efficiency for Practical ReRAM-based DNN Accelerators., , , , , , , , , and 2 other author(s). CoRR, (2023)Towards Efficient and Robust Deep Neural Network Models.. Duke University, Durham, NC, USA, (2022)base-search.net (ftdukeunivdsp:oai:localhost:10161/25173).