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EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices., , , , and . CoRR, (2019)It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation., , , and . HotMobile, page 15-21. ACM, (2021)MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edge., , and . ISCC, page 411-416. IEEE, (2023)A Throughput-Latency Co-Optimised Cascade of Convolutional Neural Network Classifiers., , and . DATE, page 1656-1661. IEEE, (2020)Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference., , , , , , and . CoRR, (2024)FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout., , , , , and . NeurIPS, page 12876-12889. (2021)Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs., , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 7943-7952. PMLR, (2020)Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars., , , and . IEEE Consumer Electron. Mag., 9 (4): 11-26 (2020)Journey Towards Tiny Perceptual Super-Resolution., , , , , , and . ECCV (26), volume 12371 of Lecture Notes in Computer Science, page 85-102. Springer, (2020)Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads., , , and . MICRO, page 353-366. ACM, (2023)