Real-time operating systems for the embedded world

An Adaptive Scheduler for Real-Time Operating Systems to Extend WSN Nodes Lifetime

Wireless Communications and Mobile Computing ◽

10.1155/2018/4185650 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Roberto Rodriguez-Zurrunero ◽

Ramiro Utrilla ◽

Elena Romero ◽

Alvaro Araujo

Keyword(s):

Operating System ◽

Real Time ◽

Operating Systems ◽

Research Area ◽

Rechargeable Batteries ◽

Wireless Sensor ◽

Portable Devices ◽

Wireless Devices ◽

Changing Environments ◽

Tasks Scheduling

Wireless Sensor Networks (WSNs) are a growing research area as a large of number portable devices are being developed. This fact makes operating systems (OS) useful to homogenize the development of these devices, to reduce design times, and to provide tools for developing complex applications. This work presents an operating system scheduler for resource-constraint wireless devices, which adapts the tasks scheduling in changing environments. The proposed adaptive scheduler allows dynamically delaying the execution of low priority tasks while maintaining real-time capabilities on high priority ones. Therefore, the scheduler is useful in nodes with rechargeable batteries, as it reduces its energy consumption when battery level is low, by delaying the least critical tasks. The adaptive scheduler has been implemented and tested in real nodes, and the results show that the nodes lifetime could be increased up to 70% in some scenarios at the expense of increasing latency of low priority tasks.

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On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures

Electronics ◽

10.3390/electronics10060689 ◽

2021 ◽

Vol 10 (6) ◽

pp. 689

Author(s):

Tom Springer ◽

Elia Eiroa-Lledo ◽

Elizabeth Stevens ◽

Erik Linstead

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Real Time ◽

Operating Systems ◽

System On Chip ◽

Low Latency ◽

Management Framework ◽

On Chip ◽

Specialized Hardware ◽

Deterministic Behavior

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.

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