Paper Info
Title
Bonseyes AI Pipeline—Bringing AI to You: End-to-end integration of data, algorithms, and deployment tools
Abstract
AbstractNext generation of embedded Information and Communication Technology (ICT) systems are interconnected and collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one of the keys for the next technological revolution: the seamless integration of AI in our daily life. However, training and deployment of custom AI solutions on embedded devices require a fine-grained integration of data, algorithms, and tools to achieve high accuracy and overcome functional and non-functional requirements. Such integration requires a high level of expertise that becomes a real bottleneck for small and medium enterprises wanting to deploy AI solutions on the Edge, which, ultimately, slows down the adoption of AI on applications in our daily life.In this work, we present a modular AI pipeline as an integrating framework to bring data, algorithms, and deployment tools together. By removing the integration barriers and lowering the required expertise, we can interconnect the different stages of particular tools and provide a modular end-to-end development of AI products for embedded devices. Our AI pipeline consists of four modular main steps: (i) data ingestion, (ii) model training, (iii) deployment optimization, and (iv) the IoT hub integration. To show the effectiveness of our pipeline, we provide examples of different AI applications during each of the steps. Besides, we integrate our deployment framework, Low-Power Deep Neural Network (LPDNN), into the AI pipeline and present its lightweight architecture and deployment capabilities for embedded devices. Finally, we demonstrate the results of the AI pipeline by showing the deployment of several AI applications such as keyword spotting, image classification, and object detection on a set of well-known embedded platforms, where LPDNN consistently outperforms all other popular deployment frameworks.
Year
DOI
Venue
2020
10.1145/3403572
ACM Transactions on Internet of Things
DocType
Volume
Issue
Journal
1
4
ISSN
Citations 
PageRank 
2691-1914
1
0.35
References 
Authors
0
12
Name
Order
Citations
PageRank
Miguel de Prado110.35
Jing Su210.35
Rabia Saeed310.35
Lorenzo Keller410.35
Noelia Vállez5258.57
Andrew Anderson6409.93
David Gregg744151.95
Luca Benini8131161188.49
Tim Llewellynn910.35
nabil ouerhani1019616.23
Rozenn Dahyot1134032.62
Nuria Pazos1210.35