Design of FPGA based general purpose neural network

Artificial neural networks (ANNs, or simply NNs) are inspired by biological nervous systems and consist of simple processing units (artificial neurons) that are interconnected by weighted connections. Neural networks can be "trained" to solve problems that are difficult to solve by conventional computer algorithms. The usage of the FPGA (Field Programmable Gate Array) for neural Network implementation provides flexibility in programmable systems. For the neural network based instrument prototype in real time application, conventional specific VLSI neural chip design suffers the limitation in time and cost. With low precision artificial neural network design, FPGAs have higher speed and smaller size for real time application than the VLSI design. Several work show that FPGA are real opportunity for flexible hardware implementation of neural network and yet representation of standard neural network face some problem. The difficulty such as limit of size and architecture of neural network that can mapped on to FPGA. This paper discuss the usage of neural network implementations. Both assets and obstacles are described and various solution are outlined.