A generic reconfigurable neural network architecture as a network on chip
Neural networks are widely used in pattern recognition, security applications and data manipulation. We propose a hardware architecture for a generic neural network, using network on chip (NoC) interconnect. The proposed architecture allows for expandability, mapping of more than one logical unit onto a single physical unit, and dynamic reconfiguration based on application-specific demands. Simulation results show that this architecture has significant performance benefits over existing architectures.