Implementing Radial Basis Functions Neural Networks on the Systolic MANTRA Machine

The development of neural network models requires the study of dedicated hardware architectures. In this paper, we propose on implementation of Radial Basis Function networks, derive an architecture based on an already existing 2D-systolic machine (MANTRA). A systolic algorithm is described to implement the required functions and the suitable sequence of operations. Theoretical efficiencies are estimated on the key tasks and some guidelines are given for a best usage of the Mantra machine in the studied framework.