TinySSL: Distilled Self-Supervised Pretraining for Sub-Megabyte MCU Models
📰 ArXiv cs.AI
Learn how to apply self-supervised learning to tiny MCU models using the proposed CA-DSSL framework, overcoming key obstacles for sub-megabyte models
Action Steps
- Identify the obstacles in applying self-supervised learning to MCU-class models, including projection head dominance and representation bottleneck
- Apply the proposed CA-DSSL framework to overcome these obstacles without using labels
- Use a teacher-guided approach to distill knowledge into smaller models
- Configure the CA-DSSL framework to be capacity-aware, adapting to the limited parameters of MCU-class models
- Evaluate the performance of the resulting models on target tasks, comparing to traditional self-supervised learning methods
Who Needs to Know This
ML engineers and researchers working on edge AI applications can benefit from this technique to improve performance of tiny models on microcontrollers, while software engineers can apply this knowledge to optimize MCU-class models
Key Insight
💡 The proposed CA-DSSL framework enables effective self-supervised learning for tiny MCU models by addressing projection head dominance, representation bottleneck, and augmentation sensitivity
Share This
🚀 TinySSL: Distilled Self-Supervised Pretraining for Sub-Megabyte MCU Models! 🤖 Overcome obstacles in SSL for tiny models with CA-DSSL 📈
Key Takeaways
Learn how to apply self-supervised learning to tiny MCU models using the proposed CA-DSSL framework, overcoming key obstacles for sub-megabyte models
Full Article
Title: TinySSL: Distilled Self-Supervised Pretraining for Sub-Megabyte MCU Models
Abstract:
arXiv:2605.08241v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has transformed representation learning for large models, yet remains unexplored for microcontroller (MCU)-class models with fewer than 500K parameters. We identify three obstacles at this scale -- projection head dominance, representation bottleneck, and augmentation sensitivity -- and propose Capacity-Aware Distilled Self-Supervised Learning (CA-DSSL), a teacher-guided framework that overcomes them without labels
Abstract:
arXiv:2605.08241v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has transformed representation learning for large models, yet remains unexplored for microcontroller (MCU)-class models with fewer than 500K parameters. We identify three obstacles at this scale -- projection head dominance, representation bottleneck, and augmentation sensitivity -- and propose Capacity-Aware Distilled Self-Supervised Learning (CA-DSSL), a teacher-guided framework that overcomes them without labels
DeepCamp AI