Entropy-Based Early-Exit in a FPGA-Based Low-Precision Neural Network

In this paper, we investigate the application of early-exit strategies to fully quantized neural networks, mapped to low-complexity FPGA SoC devices. The challenge of accuracy drop with low bitwidth quantized first convolutional layer and fully connected layers has been resolved. We apply an early-exit strategy to a network model that combines weights and activation with extremely low bitwidth and binary arithmetic precision based on the ImageNet dataset. We use entropy calculations to decide which branch of the early-exit network to take. The experiments show an improvement in inferred speed of 1.52×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.52\times $$\end{document} using an early-exit system, compared with using a single primary neural network, with a slight accuracy decrease of 1.64%.