Summary of Integer-only Quantized Transformers For Embedded Fpga-based Time-series Forecasting in Aiot, by Tianheng Ling et al.
Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT
by Tianheng Ling, Chao Qian, Gregor Schiele
First submitted to arxiv on: 6 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 This paper presents a hardware accelerator design for Transformers optimized for on-device time-series forecasting in AIoT systems. The proposed accelerator integrates integer-only quantization and Quantization-Aware Training with optimized hardware designs to realize 6-bit and 4-bit quantized Transformer models, achieving precision comparable to 8-bit quantized models from related research. The authors implement a complete solution on an embedded FPGA (Xilinx Spartan-7 XC7S15) and analyze the feasibility of deploying Transformer models on embedded IoT devices, considering factors such as precision, resource utilization, timing, power, and energy consumption for on-device inference. The results show that while sufficient performance can be attained, the optimization process is not trivial, highlighting the importance of systematically exploring various optimization combinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make a type of artificial intelligence model called Transformers work better on small devices like smart home sensors or industrial controllers. The authors created a special chip that can run these models efficiently and accurately. They tested different versions of the chip and found that it can do its job well, even when using less energy than usual. However, they also learned that finding the right balance between performance and power consumption is important. |
Keywords
* Artificial intelligence * Inference * Optimization * Precision * Quantization * Time series * Transformer
