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MOHAWK: Mobility and Heterogeneity-Aware Dynamic Community Selection for Hierarchical Federated Learning., , , , , and . IoTDI, page 249-261. ACM, (2023)Enabling Software-Defined RF Convergence with a Novel Coarse-Scale Heterogeneous Processor., , , , , , , , , and 33 other author(s). ISCAS, page 443-447. IEEE, (2022)Mobile application for receipt fraud detection based on optical character recognition., , , and . ICMV, volume 11433 of SPIE Proceedings, page 114330A. SPIE, (2019)DAS: Dynamic Adaptive Scheduling for Energy-Efficient Heterogeneous SoCs., , , , , , and . IEEE Embed. Syst. Lett., 14 (1): 51-54 (2022)Teaching Edge AI at the Undergraduate Level: A Hardware-Software Co-Design Approach., and . Computer, 56 (11): 30-38 (November 2023)Model elasticity for hardware heterogeneity in federated learning systems., , , and . FedEdge@MobiCom, page 19-24. ACM, (2022)A Hardware Prototype Targeting Distributed Deep Learning for On-device Inference., , , and . CVPR Workshops, page 1600-1601. Computer Vision Foundation / IEEE, (2020)Demo Abstract: A Hardware Prototype Targeting Federated Learning with User Mobility and Device Heterogeneity., and . IoTDI, page 486-487. ACM, (2023)DAS: Dynamic Adaptive Scheduling for Energy-Efficient Heterogeneous SoCs., , , , , , and . CoRR, (2021)