Summary of Trainable Fixed-point Quantization For Deep Learning Acceleration on Fpgas, by Dingyi Dai et al.
Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs
by Dingyi Dai, Yichi Zhang, Jiahao Zhang, Zhanqiu Hu, Yaohui Cai, Qi Sun, Zhiru Zhang
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 addresses the problem of deploying deep learning models on resource-constrained devices like embedded Field-Programmable Gate Arrays (FPGAs). The authors focus on quantization, a technique that replaces floating-point numbers with more efficient fixed-point representations. While previous work has mainly focused on quantizing matrix multiplications, leaving other layers in floating-point form, this paper aims to quantize all layers, including BatchNorm and shortcuts, using fixed-point arithmetic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research is about making deep learning models work better on devices with limited resources. Right now, most models are trained using computers that can handle lots of calculations, but they don’t work as well when deployed on devices like smartphones or smart home appliances that have less powerful processors. The authors want to find a way to make these models work efficiently on these devices by representing numbers in a more compact and efficient way. |
Keywords
* Artificial intelligence * Deep learning * Quantization
