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Reducing Inference Latency with Concurrent Architectures for Image Recognition at Edge.

, , , and . EDGE, page 245-254. IEEE, (2023)

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Real-Time Image Recognition Using Collaborative IoT Devices., , , , and . ReQuEST@ASPLOS, page 4. ACM, (2018)Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices., , , , and . CoRR, (2018)Creating Robust Deep Neural Networks With Coded Distributed Computing for IoT Systems., , and . CoRR, (2021)EVA: An End-to-End Exploratory Video Analytics System., , , , , , , , , and 13 other author(s). DEEM@SIGMOD, page 8:1-8:5. ACM, (2023)Capella: Customizing Perception for Edge Devices by Efficiently Allocating FPGAs to DNNs., , , , and . FPL, page 421. IEEE, (2019)FiGO: Fine-Grained Query Optimization in Video Analytics., , , , and . SIGMOD Conference, page 559-572. ACM, (2022)EHT-SR: An Entropy-Based Hybrid Approach for Faster Super-Resolution., , , and . ISM, page 69-78. IEEE, (2023)Hydro: Adaptive Query Processing of ML Queries., , , , and . CoRR, (2024)Video analytics from edge to server: work-in-progress., , , and . CODES+ISSS, page 14:1-14:2. ACM, (2019)Edge-Tailored Perception: Fast Inferencing in-the-Edge with Efficient Model Distribution., , , , , , and . CoRR, (2020)