English(EN) DiffLUT-Net: Differentiable Training of FPGA LUT Networks with Learnable Connectivity

新方法直接在FPGA上训练神经网络

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了DiffLUT-Net，一种在现场可编程门阵列（FPGA）上直接训练神经网络的新方法。该方法能够从头开始学习查找表（LUT）的真值表条目和网络的连接性。然后，训练好的网络可以导出为可综合的Verilog代码，在各种基准测试中提供具有竞争力的精度-资源权衡，实现高效的FPGA原生推理。 AI

影响使得AI应用能够直接在硬件上进行更高效、更紧凑的神经网络推理，有望降低成本和延迟。

排序理由这是一篇详细介绍新神经网络训练方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Jiaqi Ye, Xinrui Gong, Jingcun Wang, Olga Kondrateva, Bing Li, Grace Li Zhang · 2026-09-11 04:00

DiffLUT-Net：可学习连接性的FPGA LUT网络可微分训练

arXiv:2609.09254v1 Announce Type: cross 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 pres…

本文如何被排名

报道来源 [1]

DiffLUT-Net：可学习连接性的FPGA LUT网络可微分训练

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