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Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs

Publication: Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs. Published Jan 31, 2024.

Research & Papers
arxiv.orgPublished Jan 31, 2024
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Overview

Publication: Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs. Published Jan 31, 2024.

Publication information

Publication title
Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs
Authors
Dingyi Dai, Yichi Zhang, Jiahao Zhang, Zhanqiu Hu, Yaohui Cai, Qi Sun, Zhiru Zhang
Published
Jan 31, 2024
Last publication revision
Jan 31, 2024
Subject areas
cs.LG, cs.CV
Publication source
arxiv.org · abs/2401.17544v1 ↗

Source: arXiv API · observed Oct 11, 2026.

Source links & provenance

http://arxiv.org/abs/2401.17544 ↗

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Screenshot unavailableResource information and source links remain available. DeepIntoStreams/TSFM_Finance — reviewed source website screenshot
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DeepIntoStreams/TSFM_Finance

github.com

the official implementation of the paper “Re(visiting) Time Series Foundation Models in Finance”.

GitHub stars
31
Language
Jupyter Notebook
Code license
Apache-2.0
foundation-modeltime-seriesresearch-lab
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