Reducing Feed-Forward Neural Network Processing Time Utilizing Matrix Multiplication Algorithms on Heterogeneous Distributed Systems
Ali Kattan, Rosni Abdullah, Rosalina Abdul Salam · 2009
This paper presents a work in progress that aims to reduce the overall training and processing time of feed-forward multi-layer neural networks. If the network is large processing is expensive in terms of both; time and space. In this paper, we suggest a cost-effective and presumably a faster processing technique by utilizing a heterogeneous distributed system composed of a set of commodity computers connected by a local area network. Neural network computations can be viewed as a set of matrix multiplication processes. These can be adapted to utilize the existing matrix multiplication algorithms tailored for such systems. With Java technology as an implementation means, we discuss the different factors that should be considered in order to achieve this goal highlighting some issues that might affect such a proposed implementation.