English(EN) On Orthogonal Recurrent Neural Networks Quantization

新方法实现正交循环神经网络的量化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 04:00

研究人员开发了量化正交循环神经网络（ORNNs）的方法，创建了量化近似正交RNN（QORNNs）。通过降低其权重和激活的精度，这项进展解决了在资源受限设备上部署ORNNs的挑战。所提出的策略结合了量化感知训练和正交投影，使QORNNs在具有1000个时间步长的Copy任务上达到100%的准确率，并在置换MNIST等长期依赖任务和某些物联网应用上优于其他量化模型。 AI

影响使得ORNNs能够在边缘设备上部署，可能提高在长期依赖任务上的性能。

排序理由详细介绍ORNNs新型量化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Armand Foucault (IMT), Franck Mamalet (Comue de Toulouse), Fran\c{c}ois Malgouyres (IMT) · 2026-10-26 04:00

关于正交循环神经网络量化

arXiv:2402.04012v3 Announce Type: replace-cross Abstract: In recent years, Orthogonal Recurrent Neural Networks (ORNNs) have gained popularity due to their ability to manage tasks involving long-term dependencies, such as the Copy-task, and their linear complexity. However, exist…

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