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

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\% …

Source: arXiv cs.AR (Hardware Architecture), published 2026-11-13. The summary is the Hub's own; the full story is at the source. Report a rights or accuracy concern