Stratified sampling: fast estimation of quantization effects on DNN

Deep neural networks complexity has exploded in recent years. This explosion brought new challenges in terms of memory, execution time and power requirements. One way of meeting these challenges is to use finite precision. However, this solution may result in a degradation in output quality. This degradation needs to be estimated, balancing between time and confidence in the estimation.This paper proposes a parametric method to estimate the degradation caused by finite precision in data processing oriented applications such as deep learning. This method aims to reduce the estimation time and energy requirements while maintaining confidence in the results. This method takes advantage of a priori information about the inputs to select more informative inputs. A method to obtain this a priori information is also proposed. The results obtained are similar to a simple degradation estimation with a time reduction of one order of magnitude.