Summary of On-device Ai: Quantization-aware Training Of Transformers in Time-series, by Tianheng Ling et al.
On-device AI: Quantization-aware Training of Transformers in Time-Series
by Tianheng Ling, Gregor Schiele
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 approach is proposed for optimizing the Transformer AI model for time-series forecasting tasks, which can be deployed on resource-constrained sensor devices. By leveraging Quantization-aware Training and hardware accelerators on embedded Field Programmable Gate Arrays (FPGAs), the optimized model aims to achieve a balance between performance and reduced computational requirements. This research explores the potential benefits of applying this technique to the Transformer model, which is currently the most promising AI model for time-series forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal of my research is to make the powerful Transformer AI model smaller and faster so it can run on tiny computers found in sensors and other devices that don’t have much power or memory. To do this, I want to use a special training method called Quantization-aware Training that will help shrink the model while still letting it work well. Then, I’ll put the optimized model onto these tiny computers using specialized chips called FPGAs. |
Keywords
» Artificial intelligence » Quantization » Time series » Transformer
