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.

Automated medication verification system (AMVS): System based on edge detection and CNN classification drug on embedded systems

“…Regarding model development, the Raspberry Pi provides versatility by supporting various model architectures such as TensorFlow and PyTorch, while the Coral Dev Board primarily supports standard models like EfficientNet, Inception, MobileNet, and ResNet-50. Additionally, the Raspberry Pi boasts the lowest power consumption [ 29 , 30 ]. Furthermore, deploying TensorFlow-developed models to TPU edge requires additional conversion steps.…”

Maintaining Symmetry between Convolutional Neural Network Accuracy and Performance on an Edge TPU with a Focus on Transfer Learning Adjustments

“…Other machine learning accelerators, including the NVIDIA Jetson [30], Intel Movidius [31], and Qualcomm Snapdragon [32], provide hardware support similar to the Google edge TPU. A recent survey [33] reviewed the features and performance of embedded machine learning accelerators. Although the exact structures of these devices may differ, they all accelerate the multiply-add operations that are common to neural network computations using batch operations on layer inputs, weights, and biases.…”

“…Prior work has also evaluated the performance of edge tensor processors in the context of specific applications, including network intrusion detection [38,39], animal activity classification [40], object classification [41,42], and smart greenhouse development [43]. In a comprehensive survey [33], the authors summarize the use of embedded machine learning processors, including the Coral TPU, for sensing applications. Our work does not examine any specific applications because the accuracy of a machine learning model in a specific domain may vary wildly depending on the availability of training data, the availability of powerful training hardware, and the ability to select appropriate training parameters.…”

A review: Applications of machine learning and deep learning in aerospace engineering and aero-engine engineering

“…Even experienced inspectors can miss minor flaws. Deep learning models have a lot of potential in industrial applications like defect identification, quality control, predictive maintenance, and process optimization [94]. These algorithms, which can analyse a variety of data sources (images, videos, and sensor data), may detect and classify faults automatically.…”