Sub-100KB On-Device CNN Training via Dynamic Sparse Updates and Hardware--Software Co-Design

arXiv:2611.15236v1 Announce Type: new Abstract: The increasing demand for real-time processing and personalized deep learning has made edge-device training essential but challenging due to high memory and computational costs. To address this, we propose a Convolutional Neural Network (CNN) training accelerator with a dynamic sparse update mechanism that selectively updates important parameters using a random channel strategy, avoiding costly evaluations. Combined with activation pruning, our approach reduces activation storage to just 3.4\% of the original. Our hardware features a fused forward dataflow that concurrently processes group normalization and convolution, reducing training time by 45\% through computation reordering and efficient zero-skipping.