DiffLUT-Net: Differentiable Training of FPGA LUT Networks with Learnable Connectivity — Paper

Abstract

Field-programmable gate arrays (FPGAs) enable efficient neural-network inference, but most deployment flows either accelerate multiply-accumulate operations or convert pretrained quantized models into lookup tables (LUTs). We present DiffLUT-Net, an FPGA-native network connected by six-input LUTs that are trained from scratch. We jointly learn the 64 truth-table entries of a LUT and the source to each of its six input ports using a differentiable LUT function relaxation and hardware source selection. After training, the truth tables and connections are discretized, unused logic can be pruned, and the network is exported directly as synthesizable Verilog. Across five benchmarks, DiffLUT-Net achieves favorable accuracy-resource trade-offs. These results demonstrate the effectiveness of jointly learning LUT functions and sparse connectivity for compact FPGA-native inference. The code is available at https://github.com/TUDa-HWAI/DiffLUT-Network.

Authors 6

Jiaqi Ye, Xinrui Gong, Jingcun Wang, Olga Kondrateva, Bing Li, Grace Li Zhang

Specification

Official page: arxiv.org/abs/2609.09254
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Arxiv announce type: cross
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
arXiv id: 2609.09254
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Categories: cs.LG, cs.AI, cs.AR
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
PDF: arxiv.org/pdf/2609.09254
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Primary category: cs.LG
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh

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T19

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2 h ago

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Authors: Jiaqi Ye, Xinrui Gong, Jingcun Wang

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Value	Valid from → to	Status	Source	Confidence	Extractor
Jiaqi Ye, Xinrui Gong, Jingcun Wang, Olga Kondrateva, Bing Li, Grace Li Zhang	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperDiffLUT-Net: Differentiable Training of FPGA LUT Networks with Learnable Connectivity
New paper: DiffLUT-Net: Differentiable Training of FPGA LUT Networks with Learnable Connectivity
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Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	2 h ago	1

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