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EDLAB: A Benchmark for Edge Deep Learning Accelerators., , , , , , , , , and 2 other author(s). IEEE Des. Test, 39 (3): 8-17 (2022)EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI., , , , , , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 42 (12): 4657-4670 (December 2023)Bringing AI To Edge: From Deep Learning's Perspective., , , , and . CoRR, (2020)On Hardware-Aware Design and Optimization of Edge Intelligence., , , , , , , and . IEEE Des. Test, 40 (6): 149-162 (December 2023)Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning., , , , , , , and . DAC, page 1-6. IEEE, (2023)EMNAPE: Efficient Multi-Dimensional Neural Architecture Pruning for EdgeAI., , , , , , , and . DATE, page 1-2. IEEE, (2023)FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection., , , , , , , , , and . ACM Great Lakes Symposium on VLSI, page 310-314. ACM, (2024)Crossbar-Aligned & Integer-Only Neural Network Compression for Efficient in-Memory Acceleration., , , , , and . ASP-DAC, page 234-239. ACM, (2023)HACScale: Hardware-Aware Compound Scaling for Resource-Efficient DNNs., , , , and . ASP-DAC, page 708-713. IEEE, (2022)ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems., , , , and . DAC, page 151-156. IEEE, (2021)