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Electronics
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Real-Time Machine Learning
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Real-Time Machine Learning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 4031

Special Issue Editors

Dr. Erik Linstead

E-Mail Website
Guest Editor
Fowler School of Engineering; Electrical Engineering and Computer Science, Chapman University, Orange, CA 92866, USA
Interests: machine learning; computer vision; software engineering; medical informatics
Dr. Elizabeth Stevens

E-Mail Website
Guest Editor
Fowler School of Engineering; Electrical Engineering and Computer Science, Chapman University, Orange, CA 92866, USA
Interests: data science; machine learning; medical informatics

Special Issue Information

Dear Colleagues,

In recent years, interest in machine learning and its applications has grown exponentially, with impacts from this field transcending disciplinary boundaries in both academia and industry. Despite advances in algorithms, software, and hardware, significant hurdles to deploying machine learning pipelines in real-time, embedded environments still exist. This is due in large part to constraints such as power consumption, cooling, processing capability, and requirements for determinism that can be more easily addressed in enterprise computing environments. The purpose of this Special Issue is to present original work that provides insight into how machine learning is most effectively integrated into resource-constrained computing architectures. We solicit topics from all areas of real-time machine learning, including, but not limited to, training and deployment of machine learning models on real-time systems, modeling energy efficiency of machine learning algorithms, hardware-based machine learning models, real-time software and hardware architectures for machine learning, and novel applications of machine learning designed for embedded, real-time environments.

Dr. Erik Linstead
Dr. Elizabeth Stevens
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • real-time machine learning
  • machine learning hardware architectures
  • embedded machine learning applications
  • embedded machine learning algorithms
  • energy efficient machine learning
  • resource-constrained machine learning

Published Papers (1 paper)

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Research

21 pages, 4565 KiB  
Article
On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures
by Tom Springer, Elia Eiroa-Lledo, Elizabeth Stevens and Erik Linstead
Electronics 2021, 10(6), 689; https://doi.org/10.3390/electronics10060689 - 15 Mar 2021
Cited by 2 | Viewed by 3411
Abstract
As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization [...] Read more.
As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can provide the low-latency, deterministic execution required for embedded, and potentially safety-critical, applications at the edge. Despite this, studies considering the integration of real-time operating systems, specialized hardware, and machine learning/deep learning algorithms remain limited. In particular, better mechanisms for real-time scheduling in the context of machine learning applications will prove to be critical as these technologies move to the edge. In order to address some of these challenges, we present a resource management framework designed to provide a dynamic on-device approach to the allocation and scheduling of limited resources in a real-time processing environment. These types of mechanisms are necessary to support the deterministic behavior required by the control components contained in the edge nodes. To validate the effectiveness of our approach, we applied rigorous schedulability analysis to a large set of randomly generated simulated task sets and then verified the most time critical applications, such as the control tasks which maintained low-latency deterministic behavior even during off-nominal conditions. The practicality of our scheduling framework was demonstrated by integrating it into a commercial real-time operating system (VxWorks) then running a typical deep learning image processing application to perform simple object detection. The results indicate that our proposed resource management framework can be leveraged to facilitate integration of machine learning algorithms with real-time operating systems and embedded platforms, including widely-used, industry-standard real-time operating systems. Full article
(This article belongs to the Special Issue Real-Time Machine Learning)
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