Stochastic Gaussian Function For RBF Network

Abstract

In Artificial Neural Network applications, new solutions are searched for high speed and low circuit cost for high density inputs. In this study, a new Gaussian Function calculation method is presented for Radial Basis Function Network using stochastic calculation. The Gaussian Function of the Radial Basis Function Network was obtained using a Linear Finite State Machine approach. Stochastic representations of input values and centers were applied to XOR, OR and AND gates to realize simple arithmetic operations. The accuracy of the presented method depends on the bit length of the stochastic sequences. Using this method, considerable flexibility has been provided to the designer in terms of speed and hardware cost for applications with high input data. From the FPGA application results, the recommended stochastic calculation hardware resource requirement for the Gauss Function is much less than the hardware requirement of the corresponding deterministic calculation. The proposed stochastic network can be expanded to the large scale networks for complex tasks using simple hardware architectures. Simulation results and resource usage of FPGA are demonstrated in this paper.