Lightweight Inference by Neural Network Pruning: Accuracy, Time and Comparison

This paper addresses the application of neural networks in resource constrained edge-devices. The goal is to achieve a speedup both in inference and training time, with minimal accuracy loss. More specifically, it brings to light the need for compressing current models, which are mostly developed with access to more resources that the device that the model will potential run on.