Hardware implementation of GMDH-type artificial neural networks and its use to predict approximate three-dimensional structures of proteins

Abstract

Implementation of artificial neural networks in software on general purpose computer platforms are brought to an advanced level both in terms of performance and accuracy. Nonetheless, neural networks are not so easily applied in embedded systems, specially when the fully retraining of the network is required. This paper shows the results of the implementation of artificial neural networks based on the Group Method of Data Handling (GMDH) in reconfigurable hardware, both in the steps of training and running. A hardware architecture has been developed to be applied as a co-processing unit and an example application has been used to test its functionality. The application has been developed for the prediction of approximate 3-D structures of proteins. A set of experiments have been performed on a PC using the FPGA as a co-processor accessed through sockets over the TCP/IP protocol. The design flow employed demonstrated that it is possible to implement the network in hardware to be easily applied as an accelerator in embedded systems. The experiments show that the proposed implementation is effective in finding good quality solutions for the example problem. This work represents the early results of the novel technique of applying the GMDH algorithms in hardware for solving the problem of protein structures prediction.