Summary of Co-designing Binarized Transformer and Hardware Accelerator For Efficient End-to-end Edge Deployment, by Yuhao Ji et al.
Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment
by Yuhao Ji, Chao Fang, Shaobo Ma, Haikuo Shao, Zhongfeng Wang
First submitted to arxiv on: 16 Jul 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 for efficiently deploying Transformers on resource-constrained edge devices is proposed, addressing the challenge of large model sizes hindering real-world deployment. The method co-designs algorithms, hardware, and joint optimization from three aspects: a binarized Transformer (BMT) with optimized quantization and weighted ternary weight splitting training; a streaming processor mixed binarized Transformer accelerator (BAT); and a design space exploration approach for co-optimizing algorithm and hardware. Experimental results show significant gains in throughput (up to 2.14x-49.37x) and energy efficiency (3.72x-88.53x) compared to state-of-the-art accelerators, enabling efficient end-to-end edge deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers have revolutionized AI tasks, but their large size makes it hard to use them on devices with limited resources or high latency. One solution is to make the models smaller by binarizing them. However, current methods don’t fully take into account how the hardware will handle the model. This paper proposes a new way of co-designing algorithms and hardware for edge deployment. It includes a special type of binarized Transformer called BMT, a mixed accelerator called BAT, and a method to optimize both together. The results show that this approach can significantly improve performance on edge devices. |
Keywords
* Artificial intelligence * Optimization * Quantization * Transformer
