A Survey on optimized implementation of deep learning models on the NVIDIA Jetson platform

Abstract

Abstract Design of hardware accelerators for neural network (NN) applications involves walking a tight rope amidst the constraints of low-power, high accuracy and throughput. NVIDIA’s Jetson is a promising platform for embedded machine learning which seeks to achieve a balance between the above objectives. In this paper, we provide a survey of works that evaluate and optimize neural network applications on Jetson platform. We review both hardware and algorithmic optimizations performed for running NN algorithms on Jetson and show the real-life applications where these algorithms have been applied. We also review the works that compare Jetson with similar platforms. While the survey focuses on Jetson as an exemplar embedded system, many of the ideas and optimizations will apply just as well to existing and future embedded systems. It is widely believed that the ability to run AI algorithms on low-cost, low-power platforms will be crucial for achieving the “AI for all” vision. This survey seeks to provide a glimpse of the recent progress towards that goal.