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EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices., , , , и . CoRR, (2019)MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge., , и . ISCC, стр. 411-416. IEEE, (2023)It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation., , , и . HotMobile, стр. 15-21. ACM, (2021)Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference., , , , , , и . CoRR, (2024)A Throughput-Latency Co-Optimised Cascade of Convolutional Neural Network Classifiers., , и . DATE, стр. 1656-1661. IEEE, (2020)Journey Towards Tiny Perceptual Super-Resolution., , , , , , и . ECCV (26), том 12371 из Lecture Notes in Computer Science, стр. 85-102. Springer, (2020)Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars., , , и . IEEE Consumer Electron. Mag., 9 (4): 11-26 (2020)FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout., , , , , и . NeurIPS, стр. 12876-12889. (2021)Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs., , , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 7943-7952. PMLR, (2020)TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge., , , , , и . ICML, OpenReview.net, (2024)