Implementation of Artificial Neural Network for Real Time Applications Using Field Programmable Analog Arrays

This paper presents a method of realizing artificial neural networks (ANNs) hardware implementation using field programmable analog arrays (FPAAs). A simplified realization for neurons with piecewise linear activation functions is used to reduce the complexity of the neural network architecture. A feedforward neural network is implemented using multi-chip FPAAs. Anadigm's commercially available AN221E04 FPAA chips are adopted as the platform for simulation and experiments. The FPAA based ANN classifies two groups of data with zero error at a speed of 6.0 Million Connections Per Second (MCPS). The result is more than 1400 times faster than software implementation. The ANN architecture is also expandable to perform more complicated tasks by incorporating more FPAA chips into the implementation. The programmability of the FPAA makes rapid prototyping possible.