SIRENS: A Simple Reconfigurable Neural Hardware Structure for artificial neural network implementations

Artificial neural networks are used in various applications and research areas. Mathematically inspired approaches use these types of networks to solve complex classification or function approximation tasks whereas biologically motivated models attempt to adapt desired properties from biology such as robustness or fault tolerance to technical systems and architectures. Therefore, a great variety of different models have been proposed in literature which can be separated in time-dependent and time-independent models. To verify these models and to accelerate simulations prototypes are often implemented in integrated circuits using digital or analog designs. In this work, a simple reconfigurable neural hardware structure (SIRENS) is introduced which is capable to represent several different models of neurons, time-independent and time-dependent models as well. Therefore, this system can be used for several applications (classification or simulation) and purposes (acceleration or operation). The underlying mathematical principles are presented and, furthermore, design considerations are given in this paper.