Analysis of the influence of register file size on energy consumption, code size, and execution time

2001 ◽  
Vol 20 (11) ◽  
pp. 1329-1337 ◽  
Author(s):  
L. Wehmeyer ◽  
M.K. Jain ◽  
S. Steinke ◽  
P. Marwedel ◽  
M. Balakrishnan
Keyword(s):  
Energy Consumption ◽  
Execution Time ◽  
Register File ◽  
File Size ◽  
Code Size

Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Qingzhu Wang ◽  
Xiaoyun Cui
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Execution Time ◽  
Optimal Solution ◽  
Computation Offloading ◽  
Joint Optimization ◽  
Random Strategy ◽  
Fitness Value ◽  
Cloud Server ◽  
Time And Energy

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

scholarly journals Software energy consumption prediction using software code metrics

2021 ◽  
Author(s):  
Sedef Akinli Koçak
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Prediction Models ◽  
Code Size ◽  
Research Attention ◽  
Software Products ◽  
Code Metrics ◽  
Software Code ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

In recent years, a significant amount of energy consumption of ICT products has resulted in environmental concerns. Growing demand for mobile devices, personal computers, and the widespread adaptation of cloud computing and data centers are the main drivers for the energy consumption of the ICT systems. Finding solutions for improving the energy efficiency of the systems has become an important objective for both industry and academia. In order to address the increase in ICT energy consumption, hardware technology, such as production of energy efficient processors, has been substantially improved. However, demand for energy is growing faster than improvements are being made on these energy-aware technologies. Therefore, in addition to hardware, software technologies must also be a focus of research attention. Although software does not consume energy by itself, its characteristics determine which hardware resources are made available and how much electrical energy is used. Current literature on the energy efficiency of software, highlights, in particular, a lack of measurements and models. In this dissertation, first, the relationship between software code properties and energy consumption is explored. Second, using static code metrics regression based energy consumption prediction models are investigated. Finally, the models performance are assessed using within product and cross-product energy consumption prediction approaches. For this purpose, a quantitative based retrospective cohort study was employed. As research methods, observational data collection, mining software repositories, and regression analysis were utilized. This research results show inconsistent relationships between energy consumption and code size and complexity attributes considering different types of software products. Such results provide a foundation of knowledge that static code attributes may give some insights but would not be the sole predictors of energy consumption of software products.

Research on Network-on-Chip Dynamic and Adaptive Algorithm and Choice Strategy

2014 ◽  
Vol 539 ◽  
pp. 296-302
Author(s):  
Dong Li
Keyword(s):  
Energy Consumption ◽  
Execution Time ◽  
Adaptive Algorithm ◽  
Network On Chip ◽  
Time Dimension ◽  
Choice Strategy ◽  
Bus Structure ◽  
On Chip ◽  
Mapping Scheme ◽  
Single Objective

With further increase of the number of on-chip device, the bus structure has not met the requirements. In order to make better communication between each part, the chip designers need to explore a new structure to solve the interconnection of on-chip device. The paper proposes a network-on-chip dynamic and adaptive algorithm which selects NoC platform with 2-dimension mesh as the carrier, incorporates communication energy consumption and delay into unified cost function and uses ant colony optimization to realize NOC map facing energy consumption and delay. The experiment indicates that compared with random map, single objective optimization can separately saves (30%~47 %) and ( 20%~39%) in communication energy consumption and execution time compared with random map, and joint objective optimization can further excavate the potential of time dimension in mapping scheme dominated by the energy.

scholarly journals Creating Customized CGRAs for Scientific Applications

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 445
Author(s):  
George Charitopoulos ◽  
Ioannis Papaefstathiou ◽  
Dionisios N. Pnevmatikatos
Keyword(s):  
Energy Consumption ◽  
Execution Time ◽  
Coarse Grain ◽  
Scientific Applications ◽  
Cell Array ◽  
Analysis Methods ◽  
Reconfigurable Arrays ◽  
Hardware Implementations ◽  
Application Analysis ◽  
Software Implementations

Executing complex scientific applications on Coarse Grain Reconfigurable Arrays (CGRAs) offers improvements in the execution time and/or energy consumption when compared to optimized software implementations or even fully customized hardware solutions. In this work, we explore the potential of application analysis methods in such customized hardware solutions. We offer analysis metrics from various scientific applications and tailor the results that are to be used by MC-Def, a novel Mixed-CGRA Definition Framework targeting a Mixed-CGRA architecture that leverages the advantages of CGRAs and those of FPGAs by utilizing a customized cell-array along, with a separate LUT array being used for adaptability. Additionally, we present the implementation results regarding the VHDL-created hardware implementations of our CGRA cell concerning various scientific applications.

Estimation of energy consumption and execution time in early phases of design lifecycle: an application to biomedical systems

2008 ◽  
Vol 44 (23) ◽  
pp. 1343 ◽  
Author(s):  
G. Callou ◽  
P. Maciel ◽  
E. Andrade ◽  
B. Nogueira ◽  
E. Tavares
Keyword(s):  
Energy Consumption ◽  
Execution Time ◽  
Biomedical Systems ◽  
Early Phases
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