Embedded Development Boards for Edge-AI: A Comprehensive Report

TL;DR

This paper reviews development boards suitable for running AI algorithms on edge devices, addressing the shift from cloud-based processing to edge and fog computing for IoT applications.

Contribution

It provides a comprehensive overview of available embedded development boards for Edge-AI, highlighting their features and suitability for various IoT applications.

Findings

Many boards support deep learning frameworks

Edge-AI boards vary in processing power and energy efficiency

The review aids in selecting appropriate hardware for specific Edge-AI needs

Abstract

The use of Deep Learning and Machine Learning is becoming pervasive day by day which is opening doors to new opportunities in every aspect of technology. Its application Ranges from Health-care to Self-driving Cars, Home Automation to Smart-agriculture, and Industry 4.0. Traditionally the majority of the processing for IoT applications is being done on a central cloud but that has its issues; which include latency, security, bandwidth, and privacy, etc. It is estimated that there will be around 20 Million IoT devices by 2020 which will increase problems with sending data to the cloud and doing the processing there. A new trend of processing the data on the edge of the network is emerging. The idea is to do processing as near the point of data production as possible. Doing processing on the nodes generating the data is called Edge Computing and doing processing on a layer between the cloud and the point of data production is called Fog computing. There are no standard definitions for any of these, hence they are usually used interchangeably. In this paper, we have reviewed the development boards available for running Artificial Intelligence algorithms on the Edge