Paper Info
Title
SIRENS: A Simple Reconfigurable Neural Hardware Structure for artificial neural network implementations
Abstract
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.
Year
DOI
Venue
2006
10.1109/IJCNN.2006.247211
Vancouver, BC
Keywords
Field
DocType
approximation theory,fault tolerance,neural net architecture,artificial neural network,complex classification,fault tolerance,function approximation tasks,integrated circuits,mathematical principles,simple reconfigurable neural hardware structure
Nervous system network models,Physical neural network,Computer science,Stochastic neural network,Robustness (computer science),Types of artificial neural networks,Fault tolerance,Time delay neural network,Artificial intelligence,Artificial neural network,Machine learning
Conference
ISSN
ISBN
Citations 
2161-4393
0-7803-9490-9
2
PageRank 
References 
Authors
0.80
7
3
Name
Order
Citations
PageRank
Ralf Eickhoff16512.37
Kaulmann, T.220.80
U. Rückert3755103.61