Design and Evaluation of a New Machine Learning Framework for IoT and Embedded Devices

Gianluca Cornetta,Abdellah Touhafi

doi:10.3390/electronics10050600

Gianluca Cornetta, Abdellah Touhafi

Open Access

https://doi.org/10.3390/electronics10050600

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Abstract

Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.

Highlights

Introduction published maps and institutional affilThe Internet of Things (IoT) and Artificial Intelligence (AI) are probably two of the most popular research topics at present, driving the interest of both the academic and industrial sectors
Data science offers a rich set of algorithms to deal with classification, prediction and analysis; not all of them are suitable to run in constrained devices, as is required in IoT scenarios
The performance of a new Machine Learning (ML) framework targeted to IoT and embedded devices was stressed on Raspberry Pi III and IV boards

Summary

Introduction

The Internet of Things (IoT) and Artificial Intelligence (AI) are probably two of the most popular research topics at present, driving the interest of both the academic and industrial sectors. The reason for this is their transversality, which makes them suitable for almost every existing application. Machine learning can be effectively used to: iations

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