EnSights: A tool for energy aware software development

Alvi, Hamza Mustafa; Sahar, Hareem; Bangash, Abdul Ali; Beg, Mirza Omer

doi:10.1109/icet.2017.8281713

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…Analyzation of Specific Language Optimization Energy Measurement Diverse Programs Alvi et al [12] No No Yes No Liu et al [14] Yes Yes Yes Yes Eder et al [13] No Yes No No Pereira et al [11] No No Yes Yes Garus 3 Yes No Yes No Abdulsalam et al [16] No Yes Yes No Pereira et al [18] No No Yes No Bree et al [ The main gap between other papers and our paper is the comparison of different ways to program within Python. What we observed mostly in the literature review is the findings on which language is more energy efficient, or just how energy can be measured in software programs.…”

Section: Authorsmentioning

confidence: 88%

“…In the previous year, a tool named 'EnSights' was proposed to make efficient mobile applications with increasing the efficiency of Android Studio IDE. They used matrix methods to make energy-efficient code and applied their 'EnSights' method to the previous code and observed the comparison of previous matrices of the code and currently prepared matrices with the coefficient and correlation techniques [12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, there have been numerous studies [11][12][13][14][15][16][17][18][19][20][21][22][23] that focused on energy efficiency in software development. These studies have investigated various aspects of programming languages, tools, and…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

Reya,

Ahmed,

Zaman

et al. 2023

AETiC

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The increased use of software applications has resulted in a surge in energy demand, particularly in data centers and IT infrastructures. As global energy consumption is projected to surpass supply by 2030, the need to optimize energy consumption in programming has become imperative. Our study explores the energy efficiency of various coding patterns and techniques in Python, with the objective of guiding programmers to a more informed and energy-conscious coding practices. The research investigates the energy consumption of a comprehensive range of topics, including data initialization, access patterns, structures, string formatting, sorting algorithms, dynamic programming and performance comparisons between NumPy and Pandas, and personal computers versus cloud computing. The major findings of our research include the advantages of using efficient data structures, the benefits of dynamic programming in certain scenarios that saves up to 0.128J of energy, and the energy efficiency of NumPy over Pandas for numerical calculations. Additionally, the study also shows that assignment operator, sequential read, sequential write and string concatenation are 2.2 times, 1.05 times, 1.3 times and 1.01 times more energy-efficient choices, respectively, compared to their alternatives for data initialization, data access patterns, and string formatting. Our findings offer guidance for developers to optimize code for energy efficiency and inspire sustainable software development practices, contributing to a greener computing industry.

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Section: Authorsmentioning

confidence: 88%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

Reya,

Ahmed,

Zaman

et al. 2023

AETiC

View full text Add to dashboard Cite

show abstract

“…For verifying a claim it is essential to pick the most relevant sentences which will in turn enable the claim to be more accurately refuted or accepted [2]. To select a good evidence sentence we use the work of top performing approaches [45] for retrieving evidence sentences from the FEVER task.…”

Section: Evidence Sentence Selectionmentioning

confidence: 99%

Few Shot Learning for Information Verification

Khalid,

Beg

2021

Preprint

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Information verification is quite a challenging task, this is because many times verifying a claim can require picking pieces of information from multiple pieces of evidence which can have a hierarchy of complex semantic relations. Previously a lot of researchers have mainly focused on simply concatenating multiple evidence sentences to accept or reject claims. These approaches are limited as evidence can contain hierarchical information and dependencies. In this research, we aim to verify facts based on evidence selected from a list of articles taken from Wikipedia. Pretrained language models such as XLNET are used to generate meaningful representations and graph-based attention and convolutions are used in such a way that the system requires little additional training to learn to verify facts.

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“…The accuracy of this work can be improved by providing huge data set collection from Pakistan and the model training over signs and rules of traffic. In Pakistan, the traffic environment is very different from other countries which is one of the biggest challenges to face in autonomous car introduction in Pakistan [3].…”

Section: Related Workmentioning

confidence: 99%

Image Classification using CNN for Traffic Signs in Pakistan

Azeem¹,

Ali²

2021

Preprint

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The autonomous automotive industry is one of the largest and most conventional projects worldwide, with many technology companies effectively designing and orienting their products towards automobile safety and accuracy. These products are performing very well over the roads in developed countries. But can fail in the first minute in an underdeveloped country because there is much difference between a developed country environment and an underdeveloped country environment. The following study proposed to train these Artificial intelligence models in environment space in an underdeveloped country like Pakistan. The proposed approach on image classification uses convolutional neural networks for image classification for the model. For model pre-training German traffic signs data set was selected then fine tuned on Pakistan's dataset. The experimental setup showed the best results and accuracy from the previously conducted experiments. In this work to increase the accuracy, more dataset was collected to increase the size of images in every class in the data set. In the future, a low number of classes are required to be further increased where more images for traffic signs are required to be collected to get more accuracy on the training of the model over traffic signs of Pakistan's most used and popular roads motorway and national highway, whose traffic signs color, size, and shapes are different from common traffic signs.

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EnSights: A tool for energy aware software development

Cited by 7 publications

References 20 publications

GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

Few Shot Learning for Information Verification

Image Classification using CNN for Traffic Signs in Pakistan

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