Uncertainty plays a key role in real-time machine learning. As a significant shift from standard deep networks, which does not consider any uncertainty formulation during its training or inference, Bayesian deep networks are being currently investigated where the network is envisaged as an ensemble of plausible models learnt by the Bayes’ formulation in response to uncertainties in sensory data. Bayesian deep networks consider each synaptic weight as a sample drawn from a probability distribution with learnt mean and variance. This letter elaborates on a hardware design that exploits cycle-to-cycle variability of oxide based Resistive Random Access Memories (RRAMs) as a means to realize such a probabilistic sampling function, instead of viewing it as a disadvantage.

中文翻译：

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利用基于氧化物的电阻 RAM 可变性进行贝叶斯神经网络硬件设计

不确定性在实时机器学习中起着关键作用。作为标准深度网络的重大转变，在其训练或推理期间不考虑任何不确定性公式，目前正在研究贝叶斯深度网络，其中网络被设想为贝叶斯公式学习的合理模型的集合，以应对不确定性在感官数据中。贝叶斯深度网络将每个突触权重视为从具有学习均值和方差的概率分布中抽取的样本。这封信详细阐述了一种硬件设计，该设计利用基于氧化物的电阻式随机存取存储器 (RRAM) 的周期间可变性作为实现这种概率采样功能的手段，而不是将其视为劣势。