研究神经网络中白噪声对分类性能的影响及降噪方法。
Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies
- 在神经元级添加高斯白噪声，模拟硬件实现中的干扰。
- 调整降噪技术后，显著提升噪声环境下分类准确率。
- 适合关注神经网络硬件部署与鲁棒性设计的研究者。
近年来，利用物理耦合和模拟神经元实现神经网络的硬件方案日益重要。这类非线性复杂物理网络在速度和能效方面具有显著优势，但相较于数字模拟，可能更易受内部噪声影响。本文研究了在神经元层面加入加性与乘性高斯白噪声时，对分类任务中网络精度的影响，尤其关注读出层包含Softmax函数的情况。针对分类任务这一广泛场景，我们适配了多种噪声抑制技术，发现这些调整后的方法能有效缓解噪声带来的负面影响。
原文摘要 · Abstract (English)
In recent years, the hardware implementation of neural networks, leveraging physical coupling and analog neurons has substantially increased in relevance. Such nonlinear and complex physical networks provide significant advantages in speed and energy efficiency, but are potentially susceptible to internal noise when compared to digital emulations of such networks. In this work, we consider how additive and multiplicative Gaussian white noise on the neuronal level can affect the accuracy of the network when applied for specific tasks and including a softmax function in the readout layer. We adapt several noise reduction techniques to the essential setting of classification tasks, which represent a large fraction of neural network computing. We find that these adjusted concepts are highly effective in mitigating the detrimental impact of noise.
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