Abstract
Embedded intelligence (EI) is an emerging research field and has the objective to incorporate machine learning algorithms and intelligent decision-making capabilities into mobile and embedded devices or systems. There are several challenges to be addressed to realize efficient EI implementations in hardware such as the need for: (1) high computational processing; (2) low power consumption (or high energy efficiency); and (3) scalability to accommodate different network sizes and topologies. In recent years, an emerging hardware technology which has demonstrated strong potential and capabilities for EI implementations is the FPGA (field programmable gate array) technology. This paper presents an overview and review of embedded intelligence on FPGA with a focus on applications, platforms and challenges. There are four main classification and thematic descriptors which are reviewed and discussed in this paper for EI: (1) EI techniques including machine learning and neural networks, deep learning, expert systems, fuzzy intelligence, swarm intelligence, self-organizing map (SOM) and extreme learning; (2) applications for EI including object detection and recognition, indoor localization and surveillance monitoring, and other EI applications; (3) hardware and platforms for EI; and (4) challenges for EI. The paper aims to introduce interested researchers to this area and motivate the development of practical FPGA solutions for EI deployment.
Highlights
In recent years, machine learning [1] and swarm intelligence [2] algorithms have demonstrated successes in many fields and solved practical problems in industry
This paper presents an overview and review of embedded intelligence on Field programmable gate array (FPGA) with a focus on applications, platforms and challenges
The deploying of the convolutional neural networks (CNNs) network architecture can be made in several seconds for the FPGA system, as the customized design of the deep learning processing unit that sped up the CNN models
Summary
Machine learning [1] and swarm intelligence [2] algorithms have demonstrated successes in many fields and solved practical problems in industry. FPGA-based systems have advantages of constant latency due to dedicated hardware compared with CPU and GPU systems These features for FPGA have drawn many researchers to propose the realization of machine learning and decision-making algorithms and techniques (which traditionally have been realized on centralized computational systems e.g., cloud computing) into embedded devices/systems. There are four main classification and thematic descriptors which are reviewed and discussed in this paper for EI: (1) EI techniques including machine learning and neural networks, deep learning, expert systems, fuzzy intelligence, swarm intelligence, self-organizing map (SOM) and extreme learning; (2) applications for EI including object detection and recognition, indoor localization and surveillance monitoring, and other EI applications; (3) hardware and platforms for EI; and (4) challenges for EI. The relevant works and references which correspond to the respective classification and thematic descriptors are listed to facilitate the rapid searching of the reviewed works for readers
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
