Summary of Memory-efficient Energy-adaptive Inference Of Pre-trained Models on Batteryless Embedded Systems, by Pietro Farina et al.
Memory-efficient Energy-adaptive Inference of Pre-Trained Models on Batteryless Embedded Systems
by Pietro Farina, Subrata Biswas, Eren Yıldız, Khakim Akhunov, Saad Ahmed, Bashima Islam, Kasım Sinan Yıldırım
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework called FreeML is proposed to optimize pre-trained deep neural networks (DNNs) for memory-efficient and energy-adaptive inference on batteryless systems. This framework addresses the challenges of maintaining inference progress during power failures while minimizing memory requirements. A compression technique reduces the model footprint, making it executable on extremely memory-constrained platforms. Additionally, an early exit mechanism terminates inference at any time, adapting to changing energy availability with minimal memory overhead. The proposed approach achieves significant reductions in model sizes (up to 95x), supports adaptive inference with reduced memory overhead (2.03-19.65x), and provides time and energy benefits while maintaining acceptable accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FreeML is a new way to make deep learning work on devices without batteries. It’s like a special kind of medicine that makes old models work better on tiny devices. The problem is that these devices often run out of power, so they need to be able to keep working even when the battery runs out. FreeML solves this by shrinking down big models into tiny ones that can fit in very small memory spaces. It also has a special way to stop doing certain tasks early if it’s running low on energy. This helps save time and energy while still getting good results. |
Keywords
» Artificial intelligence » Deep learning » Inference
