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Adaptive neural networks that provide a trade-off between computing costs and inference performance can be a crucial solution for edge artificial intelligence (AI) computing where resource and energy consumption are significantly constrained. Edge AIs require a fine-tuning technique to achieve target accuracy with less computation for pre-trained models on the cloud. However, a multi-exit network, which realizes adaptive inference costs, requires significant training costs because it has manydoi:10.1109/access.2020.3047799 fatcat:zgqebi3yxneb7aeaymlcsccqxi