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With the widespread deployment of convolutional neural networks (CNNs) at the edge, on-device fine-tuning has become essential for real-time model adaptation to address environmental accuracy degradation. However, on-device training is constrained by the "memory wall" energy bottleneck and numerical instability from low-precision fixed-point arithmetic. To tackle these challenges, this t
