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RT-mDL: Supporting Real-Time Mixed Deep Learning Tasks on Edge Platforms.

, , , , and . SenSys, page 1-14. ACM, (2021)

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Poster Abstract: Unifying On-device Tensor Program Optimization through Large Foundation Model., , , , and . SenSys, page 504-505. ACM, (2023)CoEdge: A Cooperative Edge System for Distributed Real-Time Deep Learning Tasks., , , , , , , and . IPSN, page 53-66. ACM, (2023)BlastNet: Exploiting Duo-Blocks for Cross-Processor Real-Time DNN Inference., , , , , and . SenSys, page 91-105. ACM, (2022)An Indoor Smart Traffic Dataset and Data Collection System: Dataset., , , , and . SenSys, page 1007-1010. ACM, (2022)Aaron: Compile-Time Kernel Adaptation for Multi-DNN Inference Acceleration on Edge GPU., , , and . SenSys, page 802-803. ACM, (2022)EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge., , , , , , , and . SenSys, page 111-124. ACM, (2023)Soar: Design and Deployment of A Smart Roadside Infrastructure System for Autonomous Driving., , , , , , , , , and 2 other author(s). MobiCom, page 139-154. ACM, (2024)RT-mDL: Supporting Real-Time Mixed Deep Learning Tasks on Edge Platforms., , , , and . SenSys, page 1-14. ACM, (2021)EdgeML: An AutoML Framework for Real-Time Deep Learning on the Edge., , , and . IoTDI, page 133-144. ACM, (2021)Harmony: Heterogeneous Multi-Modal Federated Learning through Disentangled Model Training., , , , , , , , and . MobiSys, page 530-543. ACM, (2023)