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MLST 2020

Publication: Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML. Published Mar 11, 2020.

Research & Papers
arxiv.orgPublished Mar 11, 2020
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Overview

Publication: Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML. Published Mar 11, 2020.

Publication information

Publication title
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
Authors
Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Duc Hoang, Sergo Jindariani, Edward Kreinar, Mia Liu, Vladimir Loncar, Jennifer Ngadiuba, Kevin Pedro, Maurizio Pierini, Dylan Rankin, Sheila Sagear, Sioni Summers, Nhan Tran, Zhenbin Wu
Published
Mar 11, 2020
Last publication revision
Jun 29, 2020
Venue / journal
Mach. Learn.: Sci. Technol. 2, 015001 (2020)
Subject areas
cs.LG, eess.SP, hep-ex
DOI
10.1088/2632-2153/aba042 ↗
Publication source
arxiv.org · abs/2003.06308v2 ↗

Source: arXiv API · observed Oct 11, 2026.

Source links & provenance

https://arxiv.org/abs/2003.06308 ↗

Discovery references

References explain how this resource entered the collection. They do not represent an endorsement.

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RepositoryChecked capture

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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