Benchmarking Performance of Neural Network Architectures in Conventional Industrial Hardware

Neural networks are universal function approximations used in modern methods of artificial intelligence (AI) technology. With the increasing adoption of this technology, the need to integrate neural networks into embedded systems grows. However, neural networks are not the only methods with universal approximation properties. In this contribution, we compare the performance of two neural network architectures and one alternative for approximating benchmark functions. Specifically, Multi-Layer Perceptron (MLP), temporal Convolutional Neural Network (1D-CNN), and Piece-Wise Affine function (PWA) are compared. The alternatives are trained and implemented using publicly available tools. The execution time of the resulting code is measured on a system-on-chip (SoC) ARM processor and field programmable gate array (FPGA). The CNNs were the best option for accurate approximation of high dimensional functions, while PWA is the fastest method with good accuracy for low dimensional functions.