Hardware Implementation of FIR Neural Network for Applications in Time Series Data Prediction

Time series data prediction is used in several applications in the area of science and engineering. Time series prediction models have been implemented using statistical approaches, but recently, neural networks are being applied for times series prediction due to their inherent properties and capabilities. A variation of a standard neural network called as finite impulse response (FIR) neural network has proven to be highly successful in achieving higher degree of prediction accuracy when used over various time series prediction applications. These applications are time critical and involve huge amounts of computation that are slower when run on a general purpose processor and hence, a dedicated hardware is required. In this paper, authors present hardware implementation of an FIR neural network for applications in times series data prediction. The implementation is divided into (i) off-board, where the training algorithm and neural network configuration is implemented in Matrix Laboratory (MATLAB) and simulated with various benchmark time series data set and (ii) on-board, where the entire system is modeled in a hardware description language (HDL). The simulation experiment, hardware building blocks, the implementation framework, and the hardware design flow are discussed in this paper. The hardware resource utilization and timing information are also reported in the paper.