A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Abstract

Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies-the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

Keywords: Google Coral; Nvidia Jetson; RGB camera; Raspberry Pi; computer vision; embedded systems; machine learning; sensors.

Figure 2

Average inference time in agricultural computer vision for devices used in this application.

Figure 3

Average Inference time in facial recognition for devices used in this application.

Figure 4

Avg. inference time in depth estimation for devices used in this application.

Figure 5

Average inference time in autonomous vehicle obstacle recognition in devices used in this application.

Figure 6

Average inference time in medicine and disability assistance in devices used in these applications.

Figure 7

Average inference time in safety and security in devices used in these applications.

Figure 8

Average inference time in devices used in city management applications.

Figure 9

Average inference time in embedded computer vision devices.

Figure 10

Average inference time in devices used for testing model optimization methods.

Figure 11

Average inference time in devices covered in referenced benchmark papers.

Figure 1

Paper Layout Showing the Distribution of Subjects Covered in the Review.