English(EN) Memory-Efficient Integer-Only Training of Deep Neural Networks

新的纯整数训练方法大幅降低神经网络内存使用量

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一种新颖的深度神经网络训练算法，该算法完全使用整数运算，消除了训练过程中任何阶段对浮点运算的需求。该方法将所有张量（包括权重、梯度和优化器状态）存储为INT8，与标准的FP32基线相比，训练内存大约减小了4倍。该算法已成功在各种卷积神经网络和视觉Transformer上得到验证，在CIFAR-10、CIFAR-100和ImageNet等基准测试中取得了具有竞争力的准确率。 AI

影响降低了训练深度学习模型的内存需求，有可能在不太强大的硬件上进行训练。

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arXiv cs.AI TIER_1 English(EN) · Hazem Hesham Yousef Shalby, Fabrizio Pittorino, Manuel Roveri · 2026-10-15 04:00

深度神经网络的内存高效纯整数训练

arXiv:2610.16298v1 Announce Type: new Abstract: Training neural networks in reduced precision can yield significant efficiency gains, yet all existing methods that scale to standard computer vision benchmarks retain floating-point in at least one stage, typically the weight updat…

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深度神经网络的内存高效纯整数训练

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