Hardware Implementation of Fixed-Point Convolutional Neural Network For Classification

Abstract

The Convolutional Neural Network (CNN) dominates the research area of Field Programmable Gate Arrays (FPGAs) and demonstrates its efficiency on computer vision applications. The correct predicted rate of the CNN is highly dependent on the selection of the activation functions. Thus, we intend to deploy a CNN model on Virtex-7 while varying the activation function such as ReLU, PReLU, and Tanh Exponential (TanhExp) activation functions. To this end, we will use a fixed-point representation concerning the arithmetic numbers and the piecewise linear approximation regarding the TanhExp activation function. We present the speed, accuracy and hardware resources of each model of the CNN.