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Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models. (arXiv:2205.04886v1 [cs.LG])
May 11, 2022, 1:11 a.m. | Omobayode Fagbohungbe, Lijun Qian
cs.LG updates on arXiv.org arxiv.org
Analog hardware has become a popular choice for machine learning on
resource-constrained devices recently due to its fast execution and energy
efficiency. However, the inherent presence of noise in analog hardware and the
negative impact of the noise on deployed deep neural network (DNN) models limit
their usage. The degradation in performance due to the noise calls for the
novel design of DNN models that have excellent noiseresistant property,
leveraging the properties of the fundamental building block of DNN models. …
analog arxiv deep learning impact learning noise normalization
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