Deep learning with Elastic Averaging SGD

TL;DR

This paper introduces Elastic Averaging SGD, a novel parallel stochastic optimization algorithm for deep learning that enhances exploration and communication efficiency, leading to faster training and better performance.

Contribution

The paper proposes Elastic Averaging SGD with synchronous and asynchronous variants, providing stability analysis and demonstrating improved training speed and communication efficiency in deep neural networks.

Findings

Accelerates training of deep neural networks compared to baseline methods.

Enables more exploration by local workers, improving performance in deep learning.

Offers a stable asynchronous variant with proven stability conditions.

Abstract

We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints. A new algorithm is proposed in this setting where the communication and coordination of work among concurrent processes (local workers), is based on an elastic force which links the parameters they compute with a center variable stored by the parameter server (master). The algorithm enables the local workers to perform more exploration, i.e. the algorithm allows the local variables to fluctuate further from the center variable by reducing the amount of communication between local workers and the master. We empirically demonstrate that in the deep learning setting, due to the existence of many local optima, allowing more exploration can lead to the improved performance. We propose synchronous and asynchronous variants of the new algorithm. We provide the stability analysis of the asynchronous variant in the round-robin scheme and compare it with the more common parallelized method ADMM. We show that the stability of EASGD is guaranteed when a simple stability condition is satisfied, which is not the case for ADMM. We additionally propose the momentum-based version of our algorithm that can be applied in both synchronous and asynchronous settings. Asynchronous variant of the algorithm is applied to train convolutional neural networks for image classification on the CIFAR and ImageNet datasets. Experiments demonstrate that the new algorithm accelerates the training of deep architectures compared to DOWNPOUR and other common baseline approaches and furthermore is very communication efficient.