scholarly journals Estimating the Worst-Case Energy Consumption of Embedded Software

2006 ◽  
Author(s):  
R. Jayaseelan ◽  
T. Mitra ◽  
Xianfeng Li
Keyword(s):  
Energy Consumption ◽  
Embedded Software ◽  
Worst Case

Abstract and Concrete Data Type Optimizations at the UML and C/C++ Level for Dynamic Embedded Software

2010 ◽  
pp. 55-83
Author(s):  
Christos Baloukas ◽  
Marijn Temmerman ◽  
Anne Keller ◽  
Stylianos Mamagkakis ◽  
Francky Catthoor ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Embedded System ◽  
High Performance ◽  
Real Life ◽  
Holistic Approach ◽  
Embedded Software ◽  
Data Types ◽  
Design Engineers ◽  
Implementation Level ◽  
Abstraction Levels

An embedded system is a special-purpose system that performs predefined tasks, usually with very specific requirements. Since the system is dedicated to a specific task, design engineers can optimize it by exploiting very specialized knowledge, deriving an optimally customized system. Low energy consumption and high performance are both valid optimization targets to increase the value and mobility of the final system. Traditionally, conceptual embedded software models are built irrespectively of the underlying hardware platform, whereas embedded-system specialists typically start their optimization crusade from the executable code. This practice results in suboptimal implementations on the embedded platform because at the source-code level not all the inefficiencies introduced at the modelling level can be removed. In this book chapter, we describe both novel UML transformations at the modelling level and C/C++ transformations at the software implementation level. The transformations at both design abstraction levels target the data types of dynamic embedded software applications and provide optimizations guided by the relevant cost factors. Using a real life case study, we show how our transformations result in significant improvement in memory footprint, performance and energy consumption with respect to the initial implementation. Moreover, thanks to our holistic approach, we are able to identify new and non-trivial solutions that could hardly be found with the traditional design methods.

scholarly journals Worst-Case Energy Consumption: A New Challenge for Battery-Powered Critical Devices

2019 ◽  
pp. 1-1
Author(s):  
David Trilla ◽  
Carles Hernandez ◽  
Jaume Abella ◽  
Francisco J. Cazorla
Keyword(s):  
Energy Consumption ◽  
Worst Case

scholarly journals Worst-case energy consumption minimization based on interference analysis and bank mapping in multicore systems

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771668696
Author(s):  
Zhihua Gan ◽  
Zhimin Gu ◽  
Hai Tan ◽  
Mingquan Zhang ◽  
Jizan Zhang
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Multicore Systems ◽  
Worst Case ◽  
Mapping Algorithm ◽  
Fine Grained ◽  
Energy Consumption Reduction ◽  
Deadline Constraints ◽  
Core Mapping ◽  
The Impact

Energy is a scarce resource in real-time embedded systems due to the fact that most of them run on batteries. Hence, the designers should ensure that the energy constraints are satisfied in addition to the deadline constraints. This necessitates the consideration of the impact of the interference due to shared, low-level hardware resources such as the cache on the worst-case energy consumption of the tasks. Toward this aim, this article proposes a fine-grained approach to analyze the bank-level interference (bank conflict and bus access interference) on real-time multicore systems, which can reasonably estimate runtime interferences in shared cache and yield tighter worst-case energy consumption. In addition, we develop a bank-to-core mapping algorithm for reducing bank-level interference and improving the worst-case energy consumption. The experimental results demonstrate that our approach can improve the tightness of worst-case energy consumption by 14.25% on average compared to upper-bound delay approach. The bank-to-core mapping provides significant benefits in worst-case energy consumption reduction with 7.23%.

scholarly journals Modeling Natural Light Availability in Skyscraper Farms

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1684
Author(s):  
Michael Eaton ◽  
Kale Harbick ◽  
Timothy Shelford ◽  
Neil Mattson
Keyword(s):  
New York ◽  
Energy Consumption ◽  
Los Angeles ◽  
Light Availability ◽  
Electric Energy ◽  
Natural Light ◽  
Worst Case ◽  
Natural Lighting ◽  
Electric Energy Consumption ◽  
Canopy Area

Lighting is a major component of energy consumption in controlled environment agriculture (CEA) operations. Skyscraper farms (multilevel production in buildings with transparent glazing) have been proposed as alternatives to greenhouse or plant factories (opaque warehouses) to increase space-use efficiency while accessing some natural light. However, there are no previous models on natural light availability and distribution in skyscraper farms. This study employed climate-based daylight modeling software and the Typical Meteorological Year (TMY) dataset to investigate the effects of building geometry and context shading on the availability and spatial distribution of natural light in skyscraper farms in Los Angeles (LA) and New York City (NYC). Electric energy consumption for supplemental lighting in 20-storey skyscraper farms to reach a daily light integral target was calculated using simulation results. Natural lighting in our baseline skyscraper farms without surrounding buildings provides 13% and 15% of the light required to meet a target of 17 mol·m−2·day−1. More elongated buildings may meet up to 27% of the lighting requirements with natural light. However, shading from surrounding buildings can reduce available natural light considerably; in the worst case, natural light only supplies 5% of the lighting requirements. Overall, skyscraper farms require between 4 to 11 times more input for lighting than greenhouses per crop canopy area in the same location. We conclude that the accessibility of natural light in skyscraper farms in dense urban settings provides little advantage over plant factories.

Combining Automated Measurement-Based Cost Modeling With Static Worst-Case Execution-Time and Energy-Consumption Analyses

2019 ◽  
Vol 11 (2) ◽  
pp. 38-41 ◽  
Author(s):  
Volkmar Sieh ◽  
Robert Burlacu ◽  
Timo Honig ◽  
Heiko Janker ◽  
Phillip Raffeck ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Execution Time ◽  
Cost Modeling ◽  
Automated Measurement ◽  
Worst Case ◽  
Worst Case Execution Time ◽  
Time And Energy

scholarly journals Ideal and Predictable Hit Ratio for Matrix Transposition in Data Caches

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 184
Author(s):  
Alba Pedro-Zapater ◽  
Clemente Rodríguez ◽  
Juan Segarra ◽  
Rubén Gran Tejero ◽  
Víctor Viñals-Yúfera
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
High Performance ◽  
Negative Impact ◽  
Data Cache ◽  
Real Time Systems ◽  
Worst Case ◽  
Tile Size ◽  
Matrix Transposition ◽  
Time Systems

Matrix transposition is a fundamental operation, but it may present a very low and hardly predictable data cache hit ratio for large matrices. Safe (worst-case) hit ratio predictability is required in real-time systems. In this paper, we obtain the relations among the cache parameters that guarantee the ideal (predictable) data hit ratio assuming a Least-Recently-Used (LRU) data cache. Considering our analytical assessments, we compare a tiling matrix transposition to a cache oblivious algorithm, modified with phantom padding to improve its data hit ratio. Our results show that, with an adequate tile size, the tiling version results in an equal or better data hit ratio. We also analyze the energy consumption and execution time of matrix transposition on real hardware with pseudo-LRU (PLRU) caches. Our analytical hit/miss assessment enables the usage of a data cache for matrix transposition in real-time systems, since the number of misses in the worst case is bound. In general and high-performance computation, our analysis enables us to restrict the cache resources devoted to matrix transposition with no negative impact, in order to reduce both the energy consumption and the pollution to other computations.

scholarly journals Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

Computers ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 10
Author(s):  
Manal A. El Sayed ◽  
El Sayed M. Saad ◽  
Rasha F. Aly ◽  
Shahira M. Habashy
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Bin Packing ◽  
Shared Resources ◽  
Energy Aware ◽  
Task Partitioning ◽  
Worst Case ◽  
Real Time Scheduling ◽  
Static Partitioning ◽  
Time Scheduling

Multi-core processors have become widespread computing engines for recent embedded real-time systems. Efficient task partitioning plays a significant role in real-time computing for achieving higher performance alongside sustaining system correctness and predictability and meeting all hard deadlines. This paper deals with the problem of energy-aware static partitioning of periodic, dependent real-time tasks on a homogenous multi-core platform. Concurrent access of the tasks to shared resources by multiple tasks running on different cores induced a higher blocking time, which increases the worst-case execution time (WCET) of tasks and can cause missing the hard deadlines, consequently resulting in system failure. The proposed blocking-aware-based partitioning (BABP) algorithm aims to reduce the overall energy consumption while avoiding deadline violations. Compared to existing partitioning strategies, the proposed technique achieves more energy-saving. A series of experiments test the capabilities of the suggested algorithm compared to popular heuristics partitioning algorithms. A comparison was made between the most used bin-packing algorithms and the proposed algorithm in terms of energy consumption and system schedulability. Experimental results demonstrate that the designed algorithm outperforms the Worst Fit Decreasing (WFD), Best Fit Decreasing (BFD), and Similarity-Based Partitioning (SBP) algorithms of bin-packing algorithms, reduces the energy consumption of the overall system, and improves schedulability.

scholarly journals Statically Analyzing the Energy Efficiency of Software Product Lines

2021 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Marco Couto ◽  
João Paulo Fernandes ◽  
João Saraiva
Keyword(s):  
Energy Consumption ◽  
Simulated Annealing Algorithm ◽  
Traditional Approach ◽  
Software Product Lines ◽  
Product Lines ◽  
Worst Case ◽  
Software Products ◽  
Important Concern ◽  
Software Product ◽  
Development Approach

Optimizing software to become (more) energy efficient is an important concern for the software industry. Although several techniques have been proposed to measure energy consumption within software engineering, little work has specifically addressed Software Product Lines (SPLs). SPLs are a widely used software development approach, where the core concept is to study the systematic development of products that can be deployed in a variable way, e.g., to include different features for different clients. The traditional approach for measuring energy consumption in SPLs is to generate and individually measure all products, which, given their large number, is impractical. We present a technique, implemented in a tool, to statically estimate the worst-case energy consumption for SPLs. The goal is to reason about energy consumption in all products of a SPL, without having to individually analyze each product. Our technique combines static analysis and worst-case prediction with energy consumption analysis, in order to analyze products in a feature-sensitive manner: a feature that is used in several products is analyzed only once, while the energy consumption is estimated once per product. This paper describes not only our previous work on worst-case prediction, for comprehensibility, but also a significant extension of such work. This extension has been realized in two different axis: firstly, we incorporated in our methodology a simulated annealing algorithm to improve our worst-case energy consumption estimation. Secondly, we evaluated our new approach in four real-world SPLs, containing a total of 99 software products. Our new results show that our technique is able to estimate the worst-case energy consumption with a mean error percentage of 17.3% and standard deviation of 11.2%.

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