Soft Weight-Sharing for Neural Network Compression
This work addresses the need for efficient deep learning on mobile devices, offering an incremental improvement over existing compression methods.
The paper tackles neural network compression by applying soft weight-sharing to achieve competitive compression rates, enabling both quantization and pruning in a single training procedure.
The success of deep learning in numerous application domains created the de- sire to run and train them on mobile devices. This however, conflicts with their computationally, memory and energy intense nature, leading to a growing interest in compression. Recent work by Han et al. (2015a) propose a pipeline that involves retraining, pruning and quantization of neural network weights, obtaining state-of-the-art compression rates. In this paper, we show that competitive compression rates can be achieved by using a version of soft weight-sharing (Nowlan & Hinton, 1992). Our method achieves both quantization and pruning in one simple (re-)training procedure. This point of view also exposes the relation between compression and the minimum description length (MDL) principle.