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Boosting Mobile CNN Inference through Semantic Memory., , , , , , and . ACM Multimedia, page 2362-2371. ACM, (2021)Towards efficient vision transformer inference: a first study of transformers on mobile devices., , , and . HotMobile, page 1-7. ACM, (2022)SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 Inference., , , , , , , , and . ICCV, page 5796-5805. IEEE, (2023)Fast Hardware-Aware Neural Architecture Search., , , , and . CVPR Workshops, page 2959-2967. Computer Vision Foundation / IEEE, (2020)LitePred: Transferable and Scalable Latency Prediction for Hardware-Aware Neural Architecture Search., , , , , , , , and . NSDI, USENIX Association, (2024)Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models., , , and . CoRR, (2023)LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup., , , , , , , and . MobiCom, page 70:1-70:15. ACM, (2023)nn-Meter: towards accurate latency prediction of deep-learning model inference on diverse edge devices., , , , , , and . MobiSys, page 81-93. ACM, (2021)Accurate and Structured Pruning for Efficient Automatic Speech Recognition., , , , , , , , , and . INTERSPEECH, page 4104-4108. ISCA, (2023)On Modular Learning of Distributed Systems for Predicting End-to-End Latency., , , , , , , and . NSDI, page 1081-1095. USENIX Association, (2023)