  The interest in analog computation has grown tremendously in recent years due
to its fast computation speed and excellent energy efficiency, which is very
important for edge and IoT devices in the sub-watt power envelope for deep
learning inferencing. However, significant performance degradation suffered by
deep learning models due to the inherent noise present in the analog
computation can limit their use in mission-critical applications. Hence, there
is a need to understand the impact of critical model hyperparameters choice on
the resulting model noise-resistant property. This need is critical as the
insight obtained can be used to design deep learning models that are robust to
analog noise. In this paper, the impact of the learning rate, a critical design
choice, on the noise-resistant property is investigated. The study is achieved
by first training deep learning models using different learning rates.
Thereafter, the models are injected with analog noise and the noise-resistant
property of the resulting models is examined by measuring the performance
degradation due to the analog noise. The results showed there exists a sweet
spot of learning rate values that achieves a good balance between model
prediction performance and model noise-resistant property. Furthermore, the
theoretical justification of the observed phenomenon is provided.
