The modern trend of moving artificial intelligence computation near to the origin of data sources has increased the demand for new hardware and software suitable for such environments. We carried out a scoping study to find the current resources used when developing Edge AI applications. Due to the nature of the topic, the research combined scientific sources with product information and software project sources. The paper is structured as follows. In the first part, Edge AI applications are briefly discussed followed by hardware options and finally, the software used to develop AI models is described. There are various hardware products available, and we found as many as possible for this research to identify the best-known manufacturers. We describe the devices in the following categories: artificial intelligence accelerators and processors, field-programmable gate arrays, system-on-a-chip devices, system-on-modules, and full computers from development boards to servers. There seem to be three trends in Edge AI software development: neural network optimization, mobile device software and microcontroller software. We discussed these emerging fields and how the special challenges of low power consumption and machine learning computation are being taken into account. Our findings suggest that the Edge AI ecosystem is currently developing, and it has its own challenges to which vendors and developers are responding.
Tuomo Sipola, Janne Alatalo, Tero Kokkonen, Mika Rantonen
Sipola, T., Alatalo, J., Kokkonen, T., & Rantonen, M. (2022). Artificial Intelligence in the IoT Era: A Review of Edge AI Hardware and Software. 2022 31st Conference of Open Innovations Association (FRUCT), 31, 320–331.
This research was funded by the Regional Council of Central Finland/Council of Tampere Region and European Regional Development Fund as part of the Data for Utilisation – Leveraging digitalisation through modern artificial intelligence solutions and cybersecurity and coADDVA – ADDing VAlue by Computing in Manufacturing projects of JAMK University of Applied Sciences. The authors would like to thank Ms. Tuula Kotikoski for proofreading the manuscript.