A Formal Approach for Estimating Embedded System Execution Time and Energy Consumption

2009 ◽  
pp. 379-388 ◽  
Author(s):  
Gustavo Callou ◽  
Paulo Maciel ◽  
Ermeson Carneiro ◽  
Bruno Nogueira ◽  
Eduardo Tavares ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Embedded System ◽  
Execution Time ◽  
Formal Approach ◽  
Time And Energy

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 Exploring Direct Convolution Performance on the Gemmini Accelerator

2020 ◽  
Author(s):  
Caio Vieira ◽  
Arthur Lorenzon ◽  
Lucas Schnorr ◽  
Philippe Navaux ◽  
Antonio Carlos Beck
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
Building Block ◽  
Execution Time ◽  
Performance Gap ◽  
Main Building ◽  
Time And Energy ◽  
Lower Frequencies

Convolutional Neural Network (CNN) algorithms are becoming a recurrent solution to solve Computer Vision related problems. These networks employ convolutions as main building block, which greatly impact their performance since convolution is a costly operation. Due to its importance in CNN algorithms, this work evaluates convolution performance in the Gemmini accelerator and compare it to a conventional lightlyand heavily-loaded desktop CPU in terms of execution time and energy consumption. We show that Gemmini can achieve lower execution time and energy consumption when compared to a CPU even for small convolutions, and this performance gap grows with convolution size. Furthermore, we analyze the minimum Gemmini required frequency to match the same CPU execution time, and show that Gemmini can achieve the same runtime while working in much lower frequencies.

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

EXTENDED CONTROL FLOW GRAPH BASED PERFORMANCE AND ENERGY CONSUMPTION OPTIMIZATION USING SCRATCH-PAD MEMORY

2009 ◽  
Vol 18 (04) ◽  
pp. 697-711
Author(s):  
XUEXIANG WANG ◽  
HANLAI PU ◽  
JUN YANG ◽  
LONGXING SHI
Keyword(s):  
Energy Consumption ◽  
Greedy Algorithm ◽  
Directed Graph ◽  
Execution Time ◽  
Control Flow ◽  
Control Flow Graph ◽  
Flow Graph ◽  
Scratch Pad Memory ◽  
Energy Consumption Optimization ◽  
Time And Energy

A Scratch-Pad memory (SPM) allocation method to improve the performance of a specified application while reducing its energy consumption is presented in this paper. Integrated in the design is an extended control flow graph (ECFG) built directly from the application's instruction flow. The application of the design is transformed into a directed graph that consists of nodes and relationships. Likewise, to provide a solution in decreasing the overhead of moving nodes to SPM, the design is enhanced with a refined greedy algorithm based on ECFG. An experiment is conducted to prove the feasibility and efficiency of the method. The results indicate that the method indeed improves performance by an average of 11% and consumes lesser energy by an average of 28%. This is in comparison to previous research which based on the control flow graph (CFG) method. The latter was discovered to have disregarded the relationships of nodes. In conclusion, the application's execution time and energy consumption were reduced by an average up to 56% and 69% respectively, compared to a non-SPM environment.

scholarly journals Energy consumption and execution time estimation of embedded system applications

2011 ◽  
Vol 35 (4) ◽  
pp. 426-440 ◽  
Author(s):  
Gustavo Callou ◽  
Paulo Maciel ◽  
Eduardo Tavares ◽  
Ermeson Andrade ◽  
Bruno Nogueira ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Embedded System ◽  
Execution Time ◽  
Time Estimation

scholarly journals Minimizing Energy and Computation in Long-Running Software

2021 ◽  
Vol 11 (3) ◽  
pp. 1169
Author(s):  
Erol Gelenbe ◽  
Miltiadis Siavvas
Keyword(s):  
Energy Consumption ◽  
Execution Time ◽  
High Performance ◽  
Average Energy ◽  
Computation Time ◽  
Hardware Failures ◽  
Average Energy Consumption ◽  
The One ◽  
Hardware Platforms ◽  
Time And Energy

Long-running software may operate on hardware platforms with limited energy resources such as batteries or photovoltaic, or on high-performance platforms that consume a large amount of energy. Since such systems may be subject to hardware failures, checkpointing is often used to assure the reliability of the application. Since checkpointing introduces additional computation time and energy consumption, we study how checkpoint intervals need to be selected so as to minimize a cost function that includes the execution time and the energy. Expressions for both the program’s energy consumption and execution time are derived as a function of the failure probability per instruction. A first principle based analysis yields the checkpoint interval that minimizes a linear combination of the average energy consumption and execution time of the program, in terms of the classical “Lambert function”. The sensitivity of the checkpoint to the importance attributed to energy consumption is also derived. The results are illustrated with numerical examples regarding programs of various lengths and showing the relation between the checkpoint interval that minimizes energy consumption and execution time, and the one that minimizes a weighted sum of the two. In addition, our results are applied to a popular software benchmark, and posted on a publicly accessible web site, together with the optimization software that we have developed.

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