Convergence time analysis of Asynchronous Distributed Artificial Neural Networks

Artificial Neural Networks (ANNs) have drawn academy and industry attention for their ability to represent and solve complex problems. Researchers are studying how to distribute their computation to reduce their training time. However, the most common approaches in this direction are synchronous, letting computational resources sub-utilized. Asynchronous training does not have this drawback but is impacted by staled gradient updates, which have not been extended researched yet. Considering this, we experimentally investigate how stale gradients affect the convergence time and loss value of an ANN. In particular, we analyze an asynchronous distributed implementation of a Word2Vec model, in which the impact of staleness is negligible and can be ignored considering the computational speedup we achieve by allowing the staleness.