A Neural Network Engine for Resource Constrained Embedded Systems

This paper introduces a dedicated neural network engine developed for resource constrained embedded devices such as hearing aids. It implements a novel dynamic two-step scaling technique for quantizing the activations in order to minimize word size and thereby memory traffic. This technique requires neither computing a scaling factor during training nor expensive hardware for on-the-fly quantizati...