Deep neural networks often present uncertainties such as hardware- and software-derived noise and randomness. We studied the effects of such uncertainty on learning outcomes, with a particular focus on the function of graphics processing units (GPUs), and found that GPU-induced uncertainty increased learning accuracy of a certain deep neural network. When training a predictive deep neural network using only the CPU without the GPU, the learning error is higher than when training the same number of epochs using the GPU, suggesting that the GPU plays a different role in the learning process than just increasing the computational speed. Because this effect cannot be observed in learning by a simple autoencoder, it could be a phenomenon specific to certain types of neural networks. GPU-specific computational processing is more indeterminate than that by CPUs, and hardware-derived uncertainties, which are often considered obstacles that need to be eliminated, might, in some cases, be successfully incorporated into the training of deep neural networks. Moreover, such uncertainties might be interesting phenomena to consider in brain-related computational processing, which comprises a large mass of uncertain signals.

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GPU不确定性对预测性深度神经网络训练的影响

深度神经网络通常存在不确定性，例如硬件和软件衍生的噪声和随机性。我们研究了这种不确定性对学习结果的影响，特别关注图形处理单元 (GPU) 的功能，发现 GPU 引起的不确定性提高了某个深度神经网络的学习精度。仅使用 CPU 而不使用 GPU 训练预测深度神经网络时，学习误差高于使用 GPU 训练相同数量的 epoch 时，这表明 GPU 在学习过程中扮演的角色与仅提高计算速度不同. 由于在简单的自编码器学习中无法观察到这种效果，因此它可能是某些类型的神经网络所特有的现象。GPU 特定的计算处理比 CPU 更不确定，硬件衍生的不确定性通常被认为是需要消除的障碍，在某些情况下，可能会成功地纳入深度神经网络的训练。此外，在包含大量不确定信号的大脑相关计算处理中，这种不确定性可能是值得考虑的有趣现象。