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Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads.

, , , and . MICRO, page 353-366. ACM, (2023)

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EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices., , , , and . CoRR, (2019)DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device., , , , and . ACM Trans. Embed. Comput. Syst., 21 (6): 71:1-71:24 (November 2022)Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation., , and . CoRR, (2023)The Future of Consumer Edge-AI Computing., , , , and . CoRR, (2022)Multi-DNN Accelerators for Next-Generation AI Systems., , and . CoRR, (2022)NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution., , , and . CoRR, (2022)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)Journey Towards Tiny Perceptual Super-Resolution., , , , , , and . ECCV (26), volume 12371 of Lecture Notes in Computer Science, page 85-102. Springer, (2020)