On the Performance of Network Parallel Training in Artificial Neural Networks
This addresses the need for faster training in neural networks for real-time applications in domains like medicine and autonomous transportation, but it is incremental as it applies an existing parallelization method to neural networks.
The paper tackled the problem of speeding up training for increasingly complex neural networks by implementing Network Parallel Training using Cannon's Algorithm for matrix multiplication, showing that increasing processes speeds up training until communication costs become prohibitive and achieving superlinear speedup in empirical efficiency calculations.
Artificial Neural Networks (ANNs) have received increasing attention in recent years with applications that span a wide range of disciplines including vital domains such as medicine, network security and autonomous transportation. However, neural network architectures are becoming increasingly complex and with an increasing need to obtain real-time results from such models, it has become pivotal to use parallelization as a mechanism for speeding up network training and deployment. In this work we propose an implementation of Network Parallel Training through Cannon's Algorithm for matrix multiplication. We show that increasing the number of processes speeds up training until the point where process communication costs become prohibitive; this point varies by network complexity. We also show through empirical efficiency calculations that the speedup obtained is superlinear.