对前网络和一个耐噪结构的固有噪声耐受性的分析
Wenhao Lu1, Zhengyuan Zhang1, Feng Qin2
1School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore.
概括
本研究分析了模拟神经网络硬件中的噪声效应. 提出了一种新的耐噪声架构,以保持性能,尽管硬件不完美.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 推进神经网络的模拟电路实现易受硬件非理想性的影响.
- 诸如偏移电压漂移和热噪声等变化可以显著影响神经网络行为.
研究的目的:
- 在模拟硬件中量化feedforward神经网络的噪声耐受性.
- 开发一种新的网络架构,对输入噪声具有稳定性.
主要方法:
- 为平均平方误差损失推导下限和上限,以评估固有的噪声耐受性.
- 使用高斯混合模型扩展非高斯和非零平均噪声的边界.
- 设计了一个无训练,耐噪声的网络架构.
主要成果:
- 建立了用于估计前网络中的噪声耐受性的理论界限.
- 证明了一个新的架构有效地抑制了无需重新训练的噪音效应.
- 提供了一个封闭形式的表达方式,以超出设计限制的噪声耐受性.
结论:
- 硬件非理想性对模拟神经网络实现构成重大挑战.
- 拟议的抗噪架构为增强强性提供了一个实际的解决方案.
- 理论界限为网络对各种噪音类型的敏感性提供了有价值的见解.
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