Investigating the Effects of Schedulability Conditions on the Power Efficiency of Task Scheduling in an Embedded System

2010 ◽  
Author(s):  
Mohsen Bashiri ◽  
Seyed Ghassem Miremadi
Keyword(s):  
Embedded System ◽  
Task Scheduling ◽  
Power Efficiency

scholarly journals Real-Time Task Scheduling for Distributed Embedded System using MATLAB Toolboxes

2015 ◽  
Vol 8 (15) ◽  
Author(s):  
P. Sivakumar ◽  
B. Vinod ◽  
R. S. Sandhya Devi ◽  
E. R. Jayasakthi Rajkumar
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Task Scheduling ◽  
Time Task

A Review of the Scopes and Challenges of the Modern Real-Time Operating Systems

2018 ◽  
Vol 9 (1) ◽  
pp. 66-82 ◽  
Author(s):  
Amit K. Shukla ◽  
Rachit Sharma ◽  
Pranab K. Muhuri
Keyword(s):  
Operating System ◽  
Embedded System ◽  
Real Time ◽  
Task Scheduling ◽  
Operating Systems ◽  
Computational Cost ◽  
Dynamic Environments ◽  
Real Time Operating System ◽  
Starting Point ◽  
Design Challenges

A real-time operating system (RTOS) is an integral part of a real-time embedded system (RTES). Most of the RTESs work on dynamic environments, and hence, the computational cost of tasks cannot be calculated in advance. Thus, RTOSs play a significant role in the smooth operations of the RTES through efficient task scheduling schemes and resource managements. This article investigates the existing design challenges and scope of the modern RTOSs. A wide variety of latest RTOSs are discussed and elaborated in detail. A comparative study with their prospects has been explained so that interested readers can use the article as a readily available starting point for their further studies on this topic.

scholarly journals A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Weiwei Lin ◽  
Wentai Wu ◽  
James Z. Wang
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Power Efficiency ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Cloud Data ◽  
Virtual Clusters ◽  
Resource Requirements

Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual machine power efficiency-aware greedy scheduling algorithm (VPEGS). As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS. Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively.

scholarly journals Energy-Efficient Architecture for CNNs Inference on Heterogeneous FPGA

2019 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Fanny Spagnolo ◽  
Stefania Perri ◽  
Fabio Frustaci ◽  
Pasquale Corsonello
Keyword(s):  
Neural Networks ◽  
Embedded System ◽  
Convolutional Neural Networks ◽  
Energy Efficient ◽  
Power Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Low Cost ◽  
Frame Rate ◽  
Field Programmable

Due to the huge requirements in terms of both computational and memory capabilities, implementing energy-efficient and high-performance Convolutional Neural Networks (CNNs) by exploiting embedded systems still represents a major challenge for hardware designers. This paper presents the complete design of a heterogeneous embedded system realized by using a Field-Programmable Gate Array Systems-on-Chip (SoC) and suitable to accelerate the inference of Convolutional Neural Networks in power-constrained environments, such as those related to IoT applications. The proposed architecture is validated through its exploitation in large-scale CNNs on low-cost devices. The prototype realized on a Zynq XC7Z045 device achieves a power efficiency up to 135 Gops/W. When the VGG-16 model is inferred, a frame rate up to 11.8 fps is reached.

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