The dependence of microprocessor system energy consumption on software optimization

Sushko, Sergii; Chemeris, Alexander

doi:10.1109/elnano.2017.7939795

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Sushko et al [12] studied the effect of loop optimization on the power consumption of portable computing devices. They applied their study to ARMv8 architectures.…”

Section: Literature Reviewmentioning

confidence: 99%

An Experimental Approach to Estimation of the Energy Cost of Dynamic Branch Prediction in an Intel High-Performance Processor

Alqurashi

Al-Hashimi

2023

Computers

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Power and energy efficiency are among the most crucial requirements in high-performance and other computing platforms. In this work, extensive experimental methods and procedures were used to assess the power and energy efficiency of fundamental hardware building blocks inside a typical high-performance CPU, focusing on the dynamic branch predictor (DBP). The investigation relied on the Running Average Power Limit (RAPL) interface from Intel, a software tool for credibly reporting the power and energy based on instrumentation inside the CPU. We used well-known microbenchmarks under various run conditions to explore potential pitfalls and to develop precautions to raise the precision of the measurements obtained from RAPL for more reliable power estimation. The authors discuss the factors that affect the measurements and share the difficulties encountered and the lessons learned.

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“…Sushko et al [12] studied the effect of loop optimization on the power consumption of portable computing devices. They applied their study to ARMv8 architectures.…”

Section: Literature Reviewmentioning

confidence: 99%

An Experimental Approach to Estimation of the Energy Cost of Dynamic Branch Prediction in an Intel High-Performance Processor

Alqurashi

Al-Hashimi

2023

Computers

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“…The loop-oriented optimizations tested include Loop Unrolling (LU) and Loop Fission (LF) [18]. LU speeds up the code by reducing the number of long jumps at the end of each loop, and, therefore, reducing power [19].…”

Section: A High Level Optimizationsmentioning

confidence: 99%

“…LU speeds up the code by reducing the number of long jumps at the end of each loop, and, therefore, reducing power [19]. LF splits a big loop into multiple loops using shorts jumps instead long jumps [18]. At the memory management level, we tested Static Memory Allocation (SMA) and Dynamic Memory Allocation (DMA).…”

Section: A High Level Optimizationsmentioning

confidence: 99%

“…This is because static memory allocation saves the variables inside memory during the compiling process; therefore, when the program starts running, the CPU can access the variables easily and quickly, saving time [20]. In the case of LF the "for" loops are split, improving the power consumption, but the size of the code is increased, and the CPU spends more time reading the instructions from memory [18]. Fig.…”

Section: A Performance and Power Consumptionmentioning

confidence: 99%

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A Power and Performance Study of CompactL-Band Total Power Radiometers for UAV Remote Sensing Based in the Processing on ZYNQ and ARM Architectures

Romo¹,

Rodríguez-Solís²,

Reyes³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper presents a study on the power consumption and performance analysis of a small, portable, and ultra-low power total power L-band radiometer. The study explores two processing architectures: the ZYNQ 7010 and the ARM A8 embedded microprocessor. The processing algorithm based in C++ was tested for different clock frequencies, ADC sampling speeds, and sizes of the ADC buffer. To reduce the power consumption and the algorithm execution time, high-level and system-level optimizations, along with fixed-point Q(16,16) data representation, were applied to the main code running on LINUX Debian V8. In the case with the ZYNQ 7010, the optimizations had no notable impact on reducing power or execution time in comparison with the ARM A8, where significant variations were measured, showing a tradeoff between power consumption and algorithm performance that limits the processing capability and the system flight time. The ZYNQ 7010 runs the algorithm faster, but the power consumption is higher than the ARM A8. Using the fixed-point Q(16,16) implementation reduced the power consumption and the execution time in both architectures.Based on these results, we developed a heuristic methodology to minimize power consumption and increase the performance. Energy consumption savings for the radiometer during 20 minutes of flight was 48%. The size of the radiometer was reduced to 30cm x 30cm x 10cm, with a weight of 1.36 kg, (3 lb) allowing the system be carried by small drones. The results were validated measuring salinity at two locations in Western Puerto Rico.

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“…Due to the proliferation of personal mobile devices and sensor-based embedded systems [2], minimizing power consumption is one of the primary challenges that today's developers face. Some research investigated the methods to reduce power consumption using software optimization because software plays an important role in device energy efficiency [3].…”

Section: Introductionmentioning

confidence: 99%

A Program Optimization Method for Embedded Software Developed Using Open Sources

Cho

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Program optimization or software optimization is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources. When developing software for embedded systems, open source libraries are usually used. An Embedded software built using a variety of open software and libraries is apt to have too many unused codes because open source libraries contain many functions and features to be used in various applications. In this paper, we describe the process of a library optimization method to reduce memory consumption for the user interface software of a set-top box product. The overall optimization process uses freeware tools developed for the Linux operating system. We devised an optimization technique to compensate for operation imperfections in the target system. The original Qt library of 19.57 MB was optimized to be 7.26 MB in program image size. In the case of the DirectFB library, 3.2 MB was reduced to 2.4 MB. The optimization process can be applied to any embedded software that are developed with open source libraries or sources.

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The dependence of microprocessor system energy consumption on software optimization

Cited by 6 publications

References 4 publications

An Experimental Approach to Estimation of the Energy Cost of Dynamic Branch Prediction in an Intel High-Performance Processor

An Experimental Approach to Estimation of the Energy Cost of Dynamic Branch Prediction in an Intel High-Performance Processor

A Power and Performance Study of CompactL-Band Total Power Radiometers for UAV Remote Sensing Based in the Processing on ZYNQ and ARM Architectures

A Program Optimization Method for Embedded Software Developed Using Open Sources

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