Summary of Low Latency Transformer Inference on Fpgas For Physics Applications with Hls4ml, by Zhixing Jiang et al.
Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml
by Zhixing Jiang, Dennis Yin, Yihui Chen, Elham E Khoda, Scott Hauck, Shih-Chieh Hsu, Ekaterina Govorkova, Philip Harris, Vladimir Loncar, Eric A. Moreno
First submitted to arxiv on: 8 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper presents an efficient implementation of transformer architectures in Field-Programmable Gate Arrays (FPGAs) using hls4ml, a tool for mapping neural networks to FPGAs. The authors demonstrate the strategy for implementing specific layers such as multi-head attention, softmax, and normalization, and evaluate three distinct models on a VU13P FPGA chip. The results show latency of less than 2us, making it suitable for real-time applications. Additionally, the compatibility with any TensorFlow-built transformer model enhances scalability and applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how to make powerful AI models work faster by using special chips called FPGAs. It’s like taking a supercomputer and shrinking it down to fit in your pocket! The researchers used a tool called hls4ml to make this happen, and tested three different AI models on the chip. They found that they could process information really fast – much faster than before – which is important for things like real-time video analysis or monitoring equipment. |
Keywords
» Artificial intelligence » Multi head attention » Softmax » Transformer
