continual learning on edge

**Continual Learning on Edge** is the **deployment of continual/incremental learning algorithms on edge devices** — enabling models to learn new tasks or adapt to distribution drift without forgetting previous knowledge, all within the tight resource constraints of edge hardware. **Edge Continual Learning Challenges** - **Memory**: Cannot store large replay buffers — need memory-efficient continual learning methods. - **Compute**: Regularization-based methods (EWC, SI) add minimal compute overhead — suitable for edge. - **Storage**: Cannot keep full copies of past models — need compact knowledge summaries. - **Methods**: Experience replay (tiny buffer), parameter isolation, knowledge distillation, elastic weight consolidation. **Why It Matters** - **Process Drift**: Semiconductor processes drift over time — edge models must adapt without redeployment. - **New Products**: When new products are introduced, edge models must learn new classes without forgetting old ones. - **Autonomous**: Edge devices in remote locations must learn continuously without human intervention. **Continual Learning on Edge** is **never stop learning, never forget** — enabling edge devices to continuously adapt while maintaining knowledge of past tasks.